Comparing performance feedback with output of performance model to calibrate resource consumption control system and improve performance criterion

ABSTRACT

Embodiments are directed to improving performance criterion in a control session. Information associated with an agent may be obtained. A predicted expenditure may be generated based on the information and a performance model. The predicted expenditure may be transformed into specific-units amounts of multiple resource types based on the characteristics information. Each specific-units amount may be transformed into a normalized-units amount based on one or more normalized-units amounts of one or more other resource types. An instruction may be provided to the agent based on the normalized-units amounts. Metrics based on monitoring the agent may be obtained. One or more portions of the metrics may be compared to one or more outputs of the performance model. One or more outputs of the model may be modified based on the comparison to increase a correlation between one or more outputs and the metrics.

TECHNICAL FIELD

The present invention relates generally to controlling resourceconsumption and, more particularly, but not exclusively, to controllingresource consumption based on a comparison of performance feedback andthe output of a performance model.

BACKGROUND

Control systems that control agents' resource consumption typicallyprovide poor estimates of individual consumption needs. For example, thetypical control system often employs inaccurate models. Control systemsthat control agents' resource consumption also typically struggle toeffectively communicate the estimated individual consumption needs. Forexample, the typical control system often employs inconsistentmeasurement units, leaving the agent to resolve discrepancies. Controlsystems that control agents' resource consumption additionally typicallystruggle to adapt to variations associated with the agents that aredifficult to measure. For example, the typical control system oftenfails to account for poor consumption measurement or tracking by anagent or for characteristic anomalies associated with the agent, such asvariances in construction.

Human specialists have sometimes attempted to intervene in attempt toaccount for the deficiencies of the typical control systems. Humanintervention, however, typically fails to accurately translate theinconsistent measurement units employed by the typical control system.Thus, it is with respect to these considerations and others that thepresent invention has been made.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments of the present innovationsare described with reference to the following drawings. In the drawings,like reference numerals refer to like parts throughout the variousfigures unless otherwise specified. For a better understanding of thedescribed innovations, reference will be made to the following DetailedDescription of the Various Embodiments, which is to be read inassociation with the accompanying drawings, wherein:

FIG. 1 illustrates a schematic representation of an example systemenvironment in which various embodiments may be implemented;

FIG. 2 shows a schematic representation of an example client computer;

FIG. 3 illustrates a schematic representation of an example networkcomputer;

FIG. 4 shows a logical architecture of an example system for controllingresource consumption;

FIG. 5 illustrates a logical representation of a portion of an exampledecision model that may be employed by the system of FIG. 4;

FIG. 6 shows a logical representation of an example performance modelrepository that may be employed by the system of FIG. 4;

FIG. 7 illustrates a logical representation of an example resource modelthat may be employed by the system of FIG. 4;

FIG. 8 shows an overview flowchart of an example process for controllingresource consumption;

FIG. 9 illustrates a logical flow diagram of an example process forinitializing and launching a control session;

FIG. 10 shows a logical flowchart of an example process for initializingor launching a control session;

FIG. 11 illustrates a logical flow diagram of an example process formodifying a control session;

FIG. 12 shows a logical flowchart of an example process for evaluatingobtained metrics associated with a control session;

FIG. 13 illustrates a logical flow diagram of an example process forassigning a performance monitor to an agent;

FIG. 14 shows a logical flowchart of an example process for generating aresource data object for a new resource; and

FIG. 15 illustrates a logical flow diagram of an example process forgenerating a resource recipe.

DETAILED DESCRIPTION OF THE VARIOUS EMBODIMENTS

Various embodiments now will be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, specific example embodiments bywhich the invention may be practiced. The embodiments may, however, beembodied in many different forms and should not be construed as limitedto the embodiments set forth herein; rather, these embodiments areprovided so that this disclosure will be thorough and complete and willfully convey the scope of the embodiments to those skilled in the art.Among other things, the various embodiments may be methods, systems,media, or devices. Accordingly, the various embodiments may take theform of an entirely hardware embodiment, an entirely softwareembodiment, or an embodiment combining software and hardware aspects.The following detailed description is, therefore, not to be taken in alimiting sense.

Throughout the specification and claims, the following terms take themeanings explicitly associated herein, unless the context clearlydictates otherwise. The phrase “in one embodiment” or “in oneimplementation” as used herein does not necessarily refer to the sameembodiment or implementation, though it may. Furthermore, the phrase “inanother embodiment” or “in another implementation” as used herein doesnot necessarily refer to a different embodiment or implementation,although it may. Thus, as described below, various embodiments orimplementations may be readily combined, without departing from thescope or spirit of the invention.

In addition, as used herein, the term “or” is an inclusive “or” operatorand is equivalent to the term “or,” unless the context clearly dictatesotherwise. The term “based on” is not exclusive and allows for beingbased on additional factors not described, unless the context clearlydictates otherwise. In addition, as used herein, the meanings of “a,”“an,” and “the” include plural references. Also, as used herein, pluralreferences are intended to also disclose the singular, unless thecontext clearly dictates otherwise. For example, the term “metrics” isemployed herein and is intended to reflect “one or more metrics” becauseonly one metric may be employed or more than one metric may be employed.Moreover, one or more outputs may include multiple outputs, modifyingthe one or more outputs may include modifying a single one of the one ormore outputs, and one or more modified outputs may include multipleoutputs with a single one of the multiple outputs having been modified.The meaning of “in” includes “in” and “on.” Further, as used herein, theterms “of” and “for” refer to both the meaning of the term “of” and themeaning of the term “for” in the sentence or phrase in which one or theother is employed (although they may have the same meaning), unless thecontext clearly dictates otherwise. For example, “a specific-unitsamount of a resource type” also teaches “a specific-units amount for aresource type.” Also, as used herein, the use of “when” and “responsiveto” do not imply that associated resultant actions are required to occurimmediately or within a particular time period. Instead, they are usedherein to indicate actions that may occur or be performed in response toone or more conditions being met, unless the context clearly dictatesotherwise. Additionally, as used herein, the use of “exemplary” does notimply that other embodiments do not perform as well or are not as worthyof illustration. Instead, the term is used herein to emphasize that eachelement or function described by the term is an example element orfunction.

For example embodiments, the following terms are also used hereinaccording to the corresponding meaning, unless the context clearlydictates otherwise.

As used herein, the term “resource” refers to an asset or service thatcan be distributed, shared, or otherwise provided. Examples of resourcesmay include electricity, fuel (for example, wood, coal, diesel,gasoline, propane, nuclear fuel rods, food, or others), fuel additives(for example, anti-gelling additives, fuel stabilizers, lead additives,antiknock additives, oil additives, oxidizers, neutron moderatingmaterials, spices, vitamins, or others), or others.

As used herein, the term “agent” refers to a resource consumer. Examplesof agents may include motors, engines, athletes, or others.

As used herein, the term “performance monitor” refers to a reviewer ofagent performance. Examples of campaign monitors may include one or morecomputers, engines, specialists, or others that may review agentperformance, intervene in control sessions on behalf of agents, provideadditional feedback to the control system, provide additional control toagents, or report agent performance to supervising entities.

As used herein, the term “specific units” refers to units of measurementin a system of measurement that defines the units and the relationshipsbetween them, with the measurement system being nationally orinternationally agreed upon by one or more government bodies. Examplesof specific units include grams, meters, liters, or others in the metricsystem, pounds, feet, gallons, or others in the United States customarysystem (USCS or USC), or pounds, feet, gallons, or others in theimperial system.

As used herein, the term “normalized units” refers to units ofmeasurement in a system of measurement that defines the units and,optionally, the relationships between them, with the measurement systembeing at least partially independent of nationally or internationallyagreed upon standards of government bodies. Examples of normalized unitsinclude serving sizes. The United States Food and Drug Administrationdefines “Reference Amounts Customarily Consumed” (RACC) tables that areused by food manufacturers to determine the label serving sizes inhousehold measures that are most appropriate to the food manufacturers'specific products using procedures in 21 C.F.R. § 101.9(b). In contrastto specific units (for example, a gram or a gallon), a normalized unit(for example, a serving size) may indicate an amount of mass, length,volume, or another measurable parameter. Also in contrast to specificunits (for example, a gram or a gallon), a normalized unit (for example,a serving size) may indicate different amounts of mass, length, volume,or another measurable parameter based on the measured object. Forexample, the FDA RACC tables suggest employing the normalized unit of aserving size to refer to an amount of fluid ounces or milliliters forjuices, a number of cups for cereals, and a number of pieces for bagels.

The following briefly describes example embodiments of the invention inorder to provide a basic understanding of some aspects of the invention.This brief description is not intended as an extensive overview. It isnot intended to identify key or critical elements or to delineate orotherwise narrow the scope. Its purpose is merely to present someconcepts in a simplified form as a prelude to the more detaileddescription that is presented later.

Briefly stated, various embodiments are directed to improvingperformance criterion in a control session. In one or more of thevarious embodiments, characteristics information associated with anagent may be obtained. In some of the various embodiments, one or moreoutputs may be generated based on the characteristics information and aperformance model. In some embodiments, the one or more outputs mayinclude a predicted expenditure amount. In some embodiments, thepredicted expenditure amount may be transformed into specific-unitsamounts of multiple resource types based on the characteristicsinformation. In some embodiments, the specific-units amount of each ofthe multiple resource types may be transformed into a normalized-unitsamount of each of the multiple resource types based on one or morenormalized-units amounts of one or more other resource types. In someembodiments, a consumption instruction may be provided to the agentbased on the normalized-units amounts of the multiple resource types. Insome embodiments, metrics that are based on the consumption instructionand a monitoring of the agent may be obtained. In some embodiments, oneor more portions of the metrics may be compared to the one or moreoutputs of the performance model. In some embodiments, the one or moreoutputs may be modified based on the comparison to increase acorrelation between the one or more outputs and the metrics. In someembodiments, a modified consumption instruction may be provided to theagent based on the one or more modified outputs.

In some embodiments, the metrics may indicate reported amounts ofmultiple resource types consumed by the agent in multiple intervals andmay include performance feedback that indicates measured values of anagent characteristic in the multiple intervals.

In some embodiments, the metrics may include performance feedback thatindicates a value of a measured agent characteristic in each of two ormore intervals in each of two or more periods. In some embodiments, thevalue of the measured agent characteristic in each of the two or moreintervals in each of the two or more periods may be evaluated to providea first agent characteristic amount for a first one of the two or moreperiods and a second agent characteristic amount for a second one of thetwo or more periods. In some embodiments, the first agent characteristicamount may be evaluated based on the second characteristic amount toprovide an obtained amount of change in the agent characteristic. Insome embodiments, the obtained amount of change in the agentcharacteristic may be compared to the one or more outputs of theperformance model. In some embodiments, the one or more outputs of theperformance model may include a value of a predicted change in the agentcharacteristic based on the predicted expenditure amount.

In some embodiments, the metrics may indicate reportedly consumedamounts of specific units of the multiple resource types in multipleintervals and may include performance feedback that indicates a value ofa measured change in an agent characteristic. In some embodiments, thereportedly consumed amount of specific units of each of the multipleresource types may be transformed into reportedly consumed amounts ofnormalized units of each of the multiple resource types based on thespecific-units amount of each of the multiple resource types and thenormalized-units amount of each of the multiple resource types. In someembodiments, the reportedly consumed amount of normalized units of eachof the multiple resource types may be transformed into a totalreportedly consumed amount of specific units of each of the multipleresource types based on one or more of the reportedly consumed amountsof normalized units of one or more other ones of the multiple resourcetypes. In some embodiments, the value of the measured change in theagent characteristic may be compared to the one or more outputs of theperformance model. In some embodiments, the one or more outputs of theperformance model may include a value of a predicted change in the agentcharacteristic based on the predicted expenditure amount and the totalreportedly consumed amounts of specific units of the multiple resourcetypes.

In some embodiments, one or more portions of the characteristicsinformation associated with the agent may indicate an impairment statusof the agent. In some embodiments, transforming the predictedexpenditure amount into the specific-units amounts of the multipleresource types may include increasing the specific-units amount of oneof the multiple resource types based on the impairment status of theagent.

In some embodiments, the normalized-units amounts of the multipleresource types may be transformed into resource distribution informationbased on one or more portions of the characteristics informationassociated with the agent. In some embodiments, one or more portions ofthe resource distribution information may be transformed into theresource consumption instruction.

In some embodiments, one or more alerts may be provided to one or moreperformance monitors based on the comparison of the one or more portionsof the metrics to the one or more outputs of the performance modelindicating that performance of the agent diverged from performance ofone or more groups of agents associated with the agent.

In some embodiments, the metrics may be obtained from a client computerof the agent.

Illustrative Operating Environment

FIG. 1 shows components of an example environment in which embodimentsof the invention may be practiced. Not all of the components may berequired to practice the invention, and variations in the arrangementand type of the components may be made without departing from the spiritor scope of the invention. As shown, system 100 of FIG. 1 includes localarea networks (LANs)/wide area networks (WANs)-(network) 110, wirelessnetwork 108, client computers 102-105, Application Server Computer 116,Application Server Computer 117, Resource Consumption Control Computer118, or others.

At least one embodiment of client computers 102-105 is described in moredetail below in conjunction with FIG. 2. In one embodiment, at leastsome of client computers 102-105 may operate over one or more wired orwireless networks, such as networks 108, or 110. Generally, clientcomputers 102-105 may include virtually any computer capable ofcommunicating over a network to send and receive information, performvarious online activities, offline actions, or others. In someembodiments, one or more of client computers 102-105 may be configuredto operate within a business or other entity to perform a variety ofservices for the business or other entity. For example, client computers102-105 may be configured to operate as a web server, firewall, clientapplication, media player, mobile telephone, game console, desktopcomputer, or others. However, client computers 102-105 are notconstrained to these services and may also be employed, for example, forend-user computing in other embodiments. It should be recognized thatmore or fewer client computers (as shown in FIG. 1) may be includedwithin a system such as described herein, and embodiments are thereforenot constrained by the number or type of client computers employed.

Computers that may operate as client computer 102 may include computersthat typically connect using a wired or wireless communications mediumsuch as personal computers, multiprocessor systems, microprocessor-basedor programmable electronic devices, network PCs, or others. In someembodiments, client computers 102-105 may include virtually any portablecomputer capable of connecting to another computer and receivinginformation, such as laptop computer 103, mobile computer 104, tabletcomputers 105, or others. However, portable computers may also includeother portable computers such as cellular telephones, display pagers,radio frequency (RF) devices, infrared (IR) devices, Personal DigitalAssistants (PDAs), handheld computers, wearable computers, integrateddevices combining one or more of the preceding computers, or others. Assuch, client computers 102-105 typically range widely in terms ofcapabilities and features. Moreover, client computers 102-105 may accessvarious computing applications, including a browser, or other web-basedapplication.

A web-enabled client computer may include a browser application that isconfigured to send requests and receive responses over the web. Thebrowser application may be configured to receive or display graphics,text, multimedia, or others, employing virtually any web-based language.In some embodiments, the browser application is enabled to employJavaScript, HyperText Markup Language (HTML), eXtensible Markup Language(XML), JavaScript Object Notation (JSON), Cascading Style Sheets (CS S),or others to display or send a message. In some embodiments, a user ofthe client computer may employ the browser application to performvarious activities over a network (online). However, another applicationmay also be used to perform various online activities.

Client computers 102-105 also may include one or more other clientapplications that are configured to receive or send content betweenanother computer. The client application may include a capability tosend or receive content or other information or signals. The clientapplication may further provide information that identifies itself,including a type, capability, name, or others. In some embodiments,client computers 102-105 may uniquely identify themselves through any ofa variety of mechanisms, including an Internet Protocol (IP) address, aphone number, Mobile Identification Number (MIN), an electronic serialnumber (ESN), a client certificate, or other device identifier. Suchinformation may be provided in one or more network packets or othercollections of data, sent between other client computers, applicationserver computer 116, application server computer 117, resourceconsumption control computer 118, or other computers.

Client computers 102-105 may further be configured to include a clientapplication that enables an end-user to log into an end-user accountthat may be managed by another computer, such as application servercomputer 116, application server computer 117, resource consumptioncontrol computer 118, or others. Such an end-user account, in someexamples, may be configured to enable the end-user to manage one or moreonline activities, including in some examples, project management,software development, system administration, configuration management,search activities, social networking activities, browse variouswebsites, communicate with other users, or others. Further, clientcomputers may be arranged to enable users to provide configurationinformation, or others, to resource consumption control computer 118.Also, client computers may be arranged to enable users to displayreports, interactive user-interfaces, or results provided by resourceconsumption control computer 118.

Wireless network 108 is configured to couple client computers 103-105and its components with network 110. Wireless network 108 may includeany of a variety of wireless sub-networks that may further overlaystand-alone ad-hoc networks or others to provide aninfrastructure-oriented connection for client computers 103-105. Suchsub-networks may include mesh networks, Wireless LANs (WLANs), cellularnetworks, or others. In one embodiment, the system may include more thanone wireless network.

Wireless network 108 may further include an autonomous system ofterminals, gateways, routers, or others connected by wireless radiolinks or others. These connectors may be configured to move freely andrandomly and organize themselves arbitrarily, such that the topology ofwireless network 108 may change rapidly.

Wireless network 108 may further employ a plurality of accesstechnologies including 2nd (2G), 3rd (3G), 4th (4G), 5th (5G) generationradio access for cellular systems, WLAN, Wireless Router (WR) mesh, orothers. Access technologies such as 2G, 3G, 4G, 5G, and future accessnetworks may enable wide area coverage for mobile computers, such asclient computers 103-105 with various degrees of mobility. In someexamples, wireless network 108 may enable a radio connection through aradio network access such as Global System for Mobile communication(GSM), General Packet Radio Services (GPRS), Enhanced Data rates for GSMEvolution (EDGE), code division multiple access (CDMA), time divisionmultiple access (TDMA), Wideband Code Division Multiple Access (WCDMA),High Speed Downlink Packet Access (HSDPA), Long Term Evolution (LTE),and others. In essence, wireless network 108 may include virtually anywireless communication mechanism by which information may travel betweenclient computers 103-105 and another computer, network, a cloud-basednetwork, a cloud instance, or others.

Network 110 is configured to couple network computers with othercomputers, including, application server computer 116, applicationserver computer 117, resource consumption control computer 118, clientcomputers 102-105 through wireless network 108, or others. Network 110is enabled to employ any form of computer readable media forcommunicating information from one electronic device to another. Also,network 110 can include the Internet in addition to local area networks(LANs), wide area networks (WANs), direct connections, such as through auniversal serial bus (USB) port, Ethernet port, or other forms ofcomputer-readable media. On an interconnected set of LANs, includingthose based on differing architectures and protocols, a router acts as alink between LANs, enabling messages to be sent from one to another. Inaddition, communication links within LANs typically include twisted wirepair or coaxial cable, while communication links between networks mayutilize analog telephone lines, full or fractional dedicated digitallines including T1, T2, T3, and T4, or other carrier mechanismsincluding, for example, E-carriers, Integrated Services Digital Networks(ISDNs), Digital Subscriber Lines (DSLs), wireless links includingsatellite links, or other communications links known to those skilled inthe art. Moreover, communication links may further employ any of avariety of digital signaling technologies, including, for example, DS-0,DS-1, DS-2, DS-3, DS-4, OC-3, OC-12, OC-48, or others. Furthermore,remote computers and other related electronic devices could be remotelyconnected to either LANs or WANs via a modem and temporary telephonelink. In one embodiment, network 110 may be configured to transportinformation of an Internet Protocol (IP).

Additionally, communication media typically embodies computer readableinstructions, data structures, program modules, or other transportmechanism and includes any information non-transitory delivery media ortransitory delivery media. By way of example, communication mediaincludes wired media such as twisted pair, coaxial cable, fiber optics,wave guides, and other wired media and wireless media such as acoustic,RF, infrared, or other wireless media.

One embodiment of application server computer 116 or application servercomputer 117 is described in more detail below in conjunction with FIG.3. Briefly, however, application server computer 116 or applicationserver computer 117 includes virtually any network computer capable ofhosting applications or providing services in network environment.

One embodiment of resource consumption control computer 118 is describedin more detail below in conjunction with FIG. 3. Briefly, however,resource consumption control computer 118 may include virtually anynetwork computer capable of providing resource consumption instructionsto one or more agents to control resource consumption by the one or moreagents, obtain metrics associated with the one or more agents, comparethe obtained metrics to the outputs of one or more performance models,calibrate resource consumption control computer 118 based on thecomparison, or provide modified resource consumption instructions to theone or more agents to improve performance criterion.

Although FIG. 1 illustrates application server computer 116, applicationserver computer 117, and resource consumption control computer 118, eachas a single computer, the innovations or embodiments are not so limited.For example, one or more functions of application server computer 116,application server computer 117, resource consumption control computer118, or others, may be distributed across one or more distinct networkcomputers. Moreover, in one or more embodiments, resource consumptioncontrol computer 118 may be implemented using a plurality of networkcomputers. Further, in one or more of the various embodiments,application server computer 116, application server computer 117, orresource consumption control computer 118 may be implemented using oneor more cloud instances in one or more cloud networks. Accordingly,these innovations and embodiments are not to be construed as beinglimited to a single environment, and other configurations and otherarchitectures are also envisaged.

Illustrative Client Computer

FIG. 2 shows one embodiment of client computer 200 that may include manymore or fewer components than those shown. Client computer 200 mayrepresent, for example, at least one embodiment of mobile computers orclient computers shown in FIG. 1.

Client computer 200 may include processor 202 in communication withmemory 204 via bus 228. Client computer 200 may also include powersupply 230, network interface 232, audio interface 256, display 250,keypad 252, illuminator 254, video interface 242, input/output interface238, haptic interface 264, global positioning systems (GPS) receiver ortransceiver 258, open air gesture interface 260, sensor interface 262(for example, a temperature interface, biometric interface,accelerometer interface, weight scale interface, or others), camera(s)240, projector 246, pointing device interface 266, processor-readablestationary storage device 234, or processor-readable removable storagedevice 236. Client computer 200 may optionally communicate with a basestation (not shown) or directly with another computer. And in someembodiments, although not shown, a gyroscope may be employed withinclient computer 200 to measuring or maintaining an orientation of clientcomputer 200.

Power supply 230 may provide power to client computer 200. Arechargeable or non-rechargeable battery may be used to provide power.The power may also be provided by an external power source, such as anAC adapter or a powered docking cradle that supplements or recharges thebattery.

Network interface 232 includes circuitry for coupling client computer200 to one or more networks and is constructed for use with one or morecommunication protocols and technologies including protocols andtechnologies that implement any portion of the Open SystemsInterconnection model (OSI model), such as global system for mobilecommunication (GSM), CDMA, time division multiple access (TDMA), UDP,TCP/IP, SMS, MMS, GPRS, WAP, UWB, WiMax, SIP/RTP, EDGE, WCDMA, LTE,UMTS, orthogonal frequency-division multiplexing (OFDM), CDMA2000,EV-DO, HSDPA, or any of a variety of other wireless communicationprotocols. Network interface 232 is sometimes known as a transceiver,transceiving device, or network interface card (NIC).

Audio interface 256 may be arranged to produce and receive audio signalssuch as the sound of a human voice. For example, audio interface 256 maybe coupled to a speaker and microphone (not shown) to enabletelecommunication with others or generate an audio acknowledgement forsome action. A microphone in audio interface 256 can also be used forinput to or control of client computer 200, e.g., using voicerecognition, detecting touch based on sound, or others.

Display 250 may be a liquid crystal display (LCD), gas plasma,electronic ink, light emitting diode (LED), Organic LED (OLED) or anyother type of light reflective or light transmissive display that can beused with a computer. Display 250 may also include a touch interface 244arranged to receive input from an object such as a stylus or a digitfrom a human hand and may use resistive, capacitive, surface acousticwave (SAW), infrared, radar, or other technologies to sense touch orgestures.

Projector 246 may be a remote handheld projector or an integratedprojector that is capable of projecting an image on a remote wall or anyother reflective object such as a remote screen.

Video interface 242 may be arranged to capture video images, such as astill photo, a video segment, an infrared video, or others. For example,video interface 242 may be coupled to a digital video camera, aweb-camera, or others. Video interface 242 may comprise a lens, an imagesensor, or other electronics. Image sensors may include a complementarymetal-oxide-semiconductor (CMOS) integrated circuit, charge-coupleddevice (CCD), or any other integrated circuit for sensing light.

Keypad 252 may comprise any input device arranged to receive input froma user. For example, keypad 252 may include a push button numeric dialor a keyboard. Keypad 252 may also include command buttons that areassociated with selecting and sending images.

Illuminator 254 may provide a status indication or provide light.Illuminator 254 may remain active for specific periods of time or inresponse to event messages. For example, when illuminator 254 is active,it may backlight the buttons on keypad 252 and stay on while the clientcomputer is powered. Also, illuminator 254 may backlight these buttonsin various patterns when particular actions are performed, such asdialing another client computer. Illuminator 254 may also cause lightsources positioned within a transparent or translucent case of theclient computer to illuminate in response to actions.

Further, client computer 200 may also comprise hardware security module(HSM) 268 for providing additional tamper resistant safeguards forgenerating, storing or using security/cryptographic information, such askeys, digital certificates, passwords, passphrases, two-factorauthentication information, or others. In some embodiments, hardwaresecurity module may be employed to support one or more standard publickey infrastructures (PKI) and may be employed to generate, manage, orstore keys pairs or others. In some embodiments, HSM 268 may be astand-alone computer or may be arranged as a hardware card that may beadded to a client computer.

Client computer 200 may also comprise input/output interface 238 forcommunicating with external peripheral devices or other computers suchas other client computers and network computers. The peripheral devicesmay include an audio headset, virtual reality headsets, display screenglasses, remote speaker system, remote speaker and microphone system, orothers. Input/output interface 238 can utilize one or more technologies,such as Universal Serial Bus (USB), Infrared, Wi-Fi™, WiMax, Bluetooth™,or others.

Input/output interface 238 may also include one or more sensors fordetermining geolocation information (e.g., GPS), monitoring electricalpower conditions (e.g., voltage sensors, current sensors, frequencysensors, and so on), monitoring weather (e.g., thermostats, barometers,anemometers, humidity detectors, precipitation scales, or others), orothers. Sensors may be one or more hardware sensors that collect ormeasure data that is external to client computer 200.

Haptic interface 264 may be arranged to provide tactile feedback to auser of the client computer. For example, the haptic interface 264 maybe employed to vibrate client computer 200 in a particular way whenanother user of a computer is calling. Sensor interface 262 may be usedto provide a temperature measurement input of a user of client computer200 or equipment associated with client computer 200 (for example, fromone or more wearable sensor or others), a temperature changing output tothe user or equipment of client computer 200, an accelerometermeasurement input (for example, from a pedometer or others), a weightinput of the user, equipment, or resources consumed by the user orequipment (for example, from a scale or others), biometric measurementinputs of the user or equipment (for example, from one or more wearablesensors or others), volumetric flow measurement inputs of resourcesconsumed or provided in one or more intake sessions to the user orequipment (for example, one or more impellers or others), or othersensor inputs that may facilitate tracking performance or one or moreother characteristics information of the user or equipment, such asactivity rating, lifestyle rating, impairment status, or others (forexample, one or more wearable sensors available under the mark FITBIT orothers). In some embodiments, the one or more sensors may be part ofclient computer 200. In other embodiments, the one or more sensors maybe separate and discrete from client computer 200. Open air gestureinterface 260 may sense physical gestures of a user of client computer200, for example, by using single or stereo video cameras, radar, agyroscopic sensor inside a computer held or worn by the user, or others.In some embodiments, camera 240 may be used to track physical eyemovements of a user of client computer 200. In some embodiments, camera240 may be used to track resources consumed or accepted during one ormore intake sessions by the user or equipment associated with clientcomputer 200. For example, one or more applications 220 in clientcomputer may perform image recognition processes with regard to one ormore images of resources captured by camera 240 to generate one or morevalues that represent one or more resource types or resource amountsconsumed by the user or equipment. As another example, the one or moreimages of resources captured by camera 240 may be communicated over oneor more networks to one or more other computers (for example, one ormore network computers 300, performance monitors 404, resourceconsumption control computers 406, or others) to generate the one ormore values that represent one or more resource types or resourceamounts consumed by the user or equipment, either based on imagerecognition processes or user inputs to one or more user interfacecomponents based on one or more displays displaying one or more portionsof the one or more images.

GPS receiver or transceiver 258 can determine the physical coordinatesof client computer 200 on the surface of the Earth, which typicallyoutputs a location as latitude and longitude values. GPS receiver ortransceiver 258 can also employ other geo-positioning mechanisms,including triangulation, assisted GPS (AGPS), Enhanced Observed TimeDifference (E-OTD), Cell Identifier (CI), Service Area Identifier (SAI),Enhanced Timing Advance (ETA), Base Station Subsystem (BSS), or others,to further determine the physical location of client computer 200 on thesurface of the Earth. It is understood that under different conditions,GPS receiver or transceiver 258 can determine a physical location forclient computer 200. In one or more embodiments, however, clientcomputer 200 may, through other components, provide other informationthat may be employed to determine a physical location of the clientcomputer, including, for example, a Media Access Control (MAC) address,IP address, or others.

In one or more of the various embodiments, one or more applications (forexample, one or more operating systems 206, performance tracking engines218, performance monitor engines 222, web browsers 226, or others) maybe arranged to employ geo-location information to select one or morelocalization features, such as one or more time zones, languages,currencies, calendar formatting, geographical regions or territories, orothers. In some of the various embodiments, localization features may beused in one or more portions of file system object meta-data, filesystem objects, file systems, user-interfaces, reports, internalprocesses, databases, or others. In some embodiments, geo-locationinformation used for selecting localization information may be providedby GPS receiver or transceiver 258. Also, in some embodiments,geo-location information may include information provided using one ormore geo-location protocols over one or more networks, such as wirelessnetwork 108, network 110, or others.

Human interface components can be peripheral devices that are physicallyseparate from client computer 200, allowing for remote input or outputto client computer 200. For example, information routed as describedhere through human interface components such as display 250 or keyboard252 can instead be routed through network interface 232 to appropriatehuman interface components located remotely. Examples of human interfaceperipheral components that may be remote include audio devices, pointingdevices, keypads, displays, cameras, projectors, and others. Theseperipheral components may communicate over a Pico Network such asBluetooth™ Zigbee™, or others. Some examples of a client computer withsuch peripheral human interface components include a wearable computer,which might include a remote pico projector along with one or morecameras that remotely communicate with a separately located clientcomputer to sense a user's gestures toward portions of an imageprojected by the pico projector onto a reflected surface such as a wallor the user's hand.

A client computer may include web browser application 226 that isconfigured to receive and to send web pages, web-based messages,graphics, text, multimedia, or others. The client computer's browserapplication may employ virtually any programming language, including awireless application protocol (WAP) messages or others. In someembodiments, the browser application is enabled to employ HandheldDevice Markup Language (HDML), Wireless Markup Language (WML),WMLScript, JavaScript, Standard Generalized Markup Language (SGML),HyperText Markup Language (HTML), eXtensible Markup Language (XML),HTMLS, or others.

Memory 204 may include RAM, ROM, or other types of memory. Memory 204illustrates an example of computer-readable storage media (devices) forstorage of information such as computer-readable instructions, datastructures, program modules, or other data. Memory 204 may store BIOS208 for controlling low-level operation of client computer 200. Thememory may also store operating system 206 for controlling the operationof client computer 200. It will be appreciated that this component mayinclude a general-purpose operating system such as a version of UNIX orLINUX™ or a specialized client computer communication operating systemsuch as Windows Phone™ or the Symbian® operating system. The operatingsystem may include or interface with a Java virtual machine module thatenables control of hardware components or operating system operationsvia Java application programs.

Memory 204 may further include one or more data storage 210, which canbe utilized by client computer 200 to store, among other things,applications 220 or other data. For example, data storage 210 may alsobe employed to store information that describes various capabilities ofclient computer 200. The information may then be provided to anotherdevice or computer based on any of a variety of methods, including beingsent as part of a header during a communication, sent upon request, orothers. Data storage 210 may also be employed to store social networkinginformation including address books, buddy lists, aliases, user profileinformation, or others. Data storage 210 may further include programcode, data, algorithms, or others, for use by a processor, such asprocessor 202 to execute and perform actions. In some embodiments, atleast some of data storage 210 might also be stored on another componentof client computer 200, including non-transitory processor-readableremovable storage device 236, processor-readable stationary storagedevice 234, or even external to the client computer. Data storage 210may include, for example, initialization information 212, controlinformation 214, metrics information 216, or others. Initializationinformation 212 may include information for or obtained by initializingone or more control sessions. Control information 214 may includeinformation obtained from resource consumption control computer 118,such as resource consumption instructions. Metrics information 216 mayinclude one or more values of one or more metrics associated with one ormore active control sessions, completed control sessions, performancemonitors, control session users or agents, or others.

Applications 220 may include computer executable instructions which,when executed by client computer 200, transmit, receive, or otherwiseprocess instructions and data. Applications 220 may include, forexample, performance tracking engine 218, performance monitor engine222, other client applications 224, web browser 226, or others thatperform actions further described below. In one or more of the variousembodiments, one or more applications 220 (for example, one or moreperformance tracking engines 218, performance monitor engines 222, otherclient applications 224, web browser 226, or others) may be separate anddiscrete from one or more other applications 220. In some of the variousembodiments, one or more applications 220 may include one or moreportions of one or more other applications 220 (for example, one or moreportions of the one or more other applications 220 may include one ormore processes, programming concepts, or others within the one or moreapplications 220). Client computers 200 may be arranged to exchangecommunications, such as queries, searches, messages, notificationmessages, event messages, alerts, performance metrics, log data, APIcalls, or others with application servers or network monitoringcomputers.

Other examples of application programs include calendars, searchprograms, email client applications, IM applications, SMS applications,Voice Over Internet Protocol (VOIP) applications, contact managers, taskmanagers, transcoders, database programs, word processing programs,security applications, spreadsheet programs, games, search programs, orothers.

Additionally, in one or more embodiments (not shown in the figures),client computer 200 may include one or more embedded logic hardwaredevices instead of one or more CPUs, such as an Application SpecificIntegrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs),Programmable Array Logics (PALs), or others. The one or more embeddedlogic hardware devices may directly execute embedded logic to performactions. Also, in one or more embodiments (not shown in the figures),client computer 200 may include one or more hardware microcontrollersinstead of one or more CPUs. In some embodiments, the one or moremicrocontrollers may directly execute their own embedded logic toperform actions and access its own internal memory and its own externalInput and Output Interfaces (e.g., hardware pins or wirelesstransceivers) to perform actions as a System On a Chip (SOC) or others.

Illustrative Network Computer

FIG. 3 shows one example embodiment of network computer 300 that may beincluded in a system implementing one or more of the variousembodiments. Network computer 300 may include many more or fewercomponents than those shown in FIG. 3. However, the components shown aresufficient to disclose an illustrative embodiment for practicing theseinnovations. Network computer 300 may represent, for example, oneembodiment of one or more of application server computer 116,application server computer 117, or resource consumption controlcomputer 118 of FIG. 1.

As shown in the figure, network computer 300 includes a processor 302that may be in communication with a memory 304 via a bus 328. In someembodiments, processor 302 may be comprised of one or more hardwareprocessors or one or more processor cores. In some cases, one or more ofthe one or more processors may be specialized processors designed toperform one or more specialized actions, such as those described herein.Network computer 300 also includes a power supply 330, network interface332, audio interface 356, display 350, keyboard 352, input/outputinterface 338, processor-readable stationary storage device 334, orprocessor-readable removable storage device 336. Power supply 330provides power to network computer 300.

Network interface 332 includes circuitry for coupling network computer300 to one or more networks and is constructed for use with one or morecommunication protocols and technologies including protocols andtechnologies that implement any portion of the Open SystemsInterconnection model (OSI model), such as global system for mobilecommunication (GSM), code division multiple access (CDMA), time divisionmultiple access (TDMA), user datagram protocol (UDP), transmissioncontrol protocol/Internet protocol (TCP/IP), Short Message Service(SMS), Multimedia Messaging Service (MMS), general packet radio service(GPRS), WAP, ultra wide band (UWB), IEEE 802.16 WorldwideInteroperability for Microwave Access (WiMax), Session InitiationProtocol/Real-time Transport Protocol (SIP/RTP), or any of a variety ofother wired or wireless communication protocols. Network interface 332is sometimes known as a transceiver, transceiving device, or networkinterface card (NIC). Network computer 300 may optionally communicatewith a base station (not shown) or directly with another computer.

Audio interface 356 is arranged to produce and receive audio signalssuch as the sound of a human voice. For example, audio interface 356 maybe coupled to a speaker and microphone (not shown) to enabletelecommunication with others or generate an audio acknowledgement forsome action. A microphone in audio interface 356 can also be used forinput to or control of network computer 300, for example, using voicerecognition.

Display 350 may be a liquid crystal display (LCD), gas plasma,electronic ink, light emitting diode (LED), Organic LED (OLED), or anyother type of light reflective or light transmissive display that can beused with a computer. Display 350 may be a handheld projector or picoprojector capable of projecting an image on a wall or another object.

Network computer 300 may also comprise input/output interface 338 forcommunicating with external devices or computers not shown in FIG. 3.Input/output interface 338 can utilize one or more wired or wirelesscommunication technologies, such as USB™, Firewire™, Wi-Fi™ WiMax,Thunderbolt™, Infrared, Bluetooth™, Zigbee™, serial port, parallel port,or others.

Also, input/output interface 338 may also include one or more sensorsfor determining geolocation information (e.g., GPS), monitoringelectrical power conditions (e.g., voltage sensors, current sensors,frequency sensors, and so on), monitoring weather (e.g., thermostats,barometers, anemometers, humidity detectors, precipitation scales, orothers), or others. Sensors may be one or more hardware sensors thatcollect or measure data that is external to network computer 300. Humaninterface components can be physically separate from network computer300, allowing for remote input or output to network computer 300. Forexample, information routed as described here through human interfacecomponents such as display 350 or keyboard 352 can instead be routedthrough the network interface 332 to appropriate human interfacecomponents located elsewhere on the network. Human interface componentsinclude any component that allows the computer to take input from, orsend output to, a human user of a computer. Accordingly, pointingdevices, such as mice, styluses, track balls, or others, may communicatethrough pointing device interface 358 to receive user input.

GPS receiver or transceiver 340 can determine the physical coordinatesof network computer 300 on the surface of the Earth, which typicallyoutputs a location as latitude and longitude values. GPS receiver ortransceiver 340 can also employ other geo-positioning mechanisms,including triangulation, assisted GPS (AGPS), Enhanced Observed TimeDifference (E-OTD), Cell Identifier (CI), Service Area Identifier (SAI),Enhanced Timing Advance (ETA), Base Station Subsystem (BSS), or others,to further determine the physical location of network computer 300 onthe surface of the Earth. It is understood that under differentconditions, GPS receiver or transceiver 340 can determine a physicallocation for network computer 300. In at least one embodiment, however,network computer 300 may, through other components, provide otherinformation that may be employed to determine a physical location of theclient computer, including, for example, a Media Access Control (MAC)address, IP address, or others.

In one or more of the various embodiments, one or more applications (forexample, one or more operating systems 306, resource classificationengines 318, resource recipe generation engines 322, consumption controlengines 324, metrics analysis engines 326, dashboards, or others) may bearranged to employ geo-location information to select one or morelocalization features, such as one or more time zones, languages,currencies, calendar formatting, geographical regions or territories, orothers. In some of the various embodiments, localization features may beused in one or more portions of file system object meta-data, filesystem objects, file systems, user-interfaces, reports, internalprocesses, databases, or others. In some embodiments, geo-locationinformation used for selecting localization information may be providedby GPS receiver or transceiver 340. Also, in some embodiments,geo-location information may include information provided using one ormore geo-location protocols over one or more networks, such as wirelessnetwork 108, network 110, or others.

Memory 304 may include Random Access Memory (RAM), Read-Only Memory(ROM), or other types of memory. Memory 304 illustrates an example ofcomputer-readable storage media (devices) for storage of informationsuch as computer-readable instructions, data structures, program modulesor other data. Memory 304 stores a basic input/output system (BIOS) 308for controlling low-level operation of network computer 300. The memoryalso stores an operating system 306 for controlling the operation ofnetwork computer 300. It will be appreciated that this component mayinclude a general-purpose operating system such as a version of UNIX orLINUX™ or a specialized operating system such as Microsoft Corporation'sWindows® operating system or the Apple Corporation's IOS® operatingsystem. The operating system may include or interface with a Javavirtual machine module that enables control of hardware components oroperating system operations via Java application programs. Likewise,other runtime environments may be included.

Memory 304 may further include one or more data storage 310, which canbe utilized by network computer 300 to store, among other things,applications 320 or other data. For example, data storage 310 may alsobe employed to store information that describes various capabilities ofnetwork computer 300. The information may then be provided to anotherdevice or computer based on any of a variety of methods, including beingsent as part of a header during a communication, sent upon request, orothers. Data storage 310 may also be employed to store social networkinginformation including address books, buddy lists, aliases, user profileinformation, or others. Data storage 310 may further include programcode, data, algorithms, or others, for use by a processor, such asprocessor 302 to execute and perform actions such as those actionsdescribed below. In some embodiments, at least some of data storage 310might also be stored on another component of network computer 300,including non-transitory media inside processor-readable removablestorage device 336, processor-readable stationary storage device 334, orany other computer-readable storage device within network computer 300or even external to network computer 300. Data storage 310 may include,for example, demographics information 312, resource information 314,metrics information 316, or others. Demographics information 312 mayinclude information indicative of characteristics, historical resourceconsumption, historical performance, or others associated with one ormore geographical regions, control sessions, agents, entities associatedwith one or more agents, populations of agents, groups within the one ormore populations of agents, or others. Resource information 314 mayinclude information indicative of characteristics associated with one ormore resources, recipes of combinations of resources, or others that maybe employed during one or more consumption control sessions. Metricsinformation 316 may include one or more values of one or more metricsassociated with one or more active control sessions, completed controlsessions, performance monitors, control session users or agents, orothers.

Applications 320 may include computer executable instructions which,when executed by network computer 300, transmit, receive, or otherwiseprocess messages (e.g., SMS, Multimedia Messaging Service (MMS), InstantMessage (IM), email, or other messages), audio, video, and enabletelecommunication with another user of another mobile computer. Otherexamples of application programs include calendars, search programs,email client applications, IM applications, SMS applications, Voice OverInternet Protocol (VOIP) applications, contact managers, task managers,transcoders, database programs, word processing programs, securityapplications, spreadsheet programs, games, search programs, databases,web services, and so forth. Applications 320 may include resourceclassification engine 318, resource recipe generation engine 322,consumption control engine 324, metrics analysis engine 326, or othersthat perform actions further described below. In one or more of thevarious embodiments, one or more applications 220 or 320 (for example,one or more performance tracking engines 218, performance monitorengines 222, other client applications 224, web browser 226, resourceclassification engines 318, resource recipe generation engines 322,consumption control engines 324, metrics analysis engines 326, orothers) may be separate and discrete from one or more other applications220 or 320. In some of the various embodiments, one or more applications220 or 320 may include one or more portions of one or more otherapplications 220 or 320 (for example, one or more portions of the one ormore other applications 220 or 320 may include one or more processes,programming concepts, or others within the one or more applications 220or 320). In some embodiments, one or more of the applications may beimplemented as modules or components of another application. Further, insome embodiments, applications may be implemented as operating systemextensions, modules, plugins, or others.

Furthermore, in some of the various embodiments, resource classificationengine 318, resource recipe generation engine 322, consumption controlengine 324, or metrics analysis engine 326 may be operative in acloud-based computing environment. In some of the various embodiments,these engines, or others, that comprise the control platform or controlsystem may be executing within virtual machines or virtual servers thatmay be managed in a cloud-based based computing environment. In some ofthe various embodiments, in this context the applications may flow fromone physical network computer within the cloud-based environment toanother depending on performance and scaling considerationsautomatically managed by the cloud computing environment. Likewise, insome of the various embodiments, virtual machines or virtual serversdedicated to resource classification engine 318, resource recipegeneration engine 322, consumption control engine 324, or metricsanalysis engine 326 may be provisioned and de-commissionedautomatically. Also, in some of the various embodiments, resourceclassification engine 318, resource recipe generation engine 322,consumption control engine 324, metrics analysis engine 326, or othersmay be located in virtual servers running in a cloud-based computingenvironment rather than being tied to one or more specific physicalnetwork computers. In some embodiments, one or more of resourceclassification engine 318, resource recipe generation engine 322,consumption control engine 324, metrics analysis engine 326, or othersmay individually or cooperatively perform one or more portions of one ormore of the actions described herein, such as one or more actionsassociated with one or more blocks in one or more of the processesdescribed herein. In some embodiments, one or more of the named engineshave sub-engines (not shown) that individually or cooperatively performone or more of the one or more actions. In some embodiments, one or moreof the named engines are included as part of another one or more of thenamed engines.

Further, network computer 300 may also comprise hardware security module(HSM) 360 for providing additional tamper resistant safeguards forgenerating, storing or using security/cryptographic information, such askeys, digital certificates, passwords, passphrases, two-factorauthentication information, or others. In some embodiments, hardwaresecurity module may be employed to support one or more standard publickey infrastructures (PKI) and may be employed to generate, manage, orstore keys pairs, or others. In some embodiments, HSM 360 may be astand-alone network computer, in other cases, HSM 360 may be arranged asa hardware card that may be installed in a network computer.

Additionally, in one or more embodiments (not shown in the figures),network computer 300 may include one or more embedded logic hardwaredevices instead of one or more CPUs, such as an Application SpecificIntegrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs),Programmable Array Logics (PALs), or others. The one or more embeddedlogic hardware devices may directly execute embedded logic to performactions. Also, in one or more embodiments (not shown in the figures),network computer 300 may include one or more hardware microcontrollersinstead of one or more CPUs. In some embodiments, the one or moremicrocontrollers may directly execute their own embedded logic toperform actions and access its own internal memory and its own externalInput and Output Interfaces (e.g., hardware pins or wirelesstransceivers) to perform actions as a System On a Chip (SOC) or others.

Illustrative Logical System Architecture

FIG. 4 shows a logical architecture of example resource consumptioncontrol system 400 for controlling resource consumption. System 400 maybe arranged to include one or more consuming agents or performancemonitors, such as agents 402 or performance monitors 404. Each of theone or more agents or performance monitors may include, or be associatedwith, one or more client computers, such as client computer 200 (forexample, one or more of client computers 102-105 or others). System 400may also be arranged to include one or more resource consumption controlcomputers, such as resource consumption control computer 406(represented in the example shown in FIG. 4 as a cloud-computingenvironment). Each of the one or more resource consumption controlcomputers may include a network computer, such as network computer 300(for example, application server computer 116, application servercomputer 117, resource consumption control computer 118, or others).Resource consumption control computer 406 may include one or moreengines, such as resource classification engine 408 (e.g., resourceclassification engine 318 or others), resource recipe engine 410 (e.g.,resource recipe generation engine 322 or others), consumption controlengine 412 (e.g., consumption control engine 324 or others), metricsanalysis engine 414 (e.g., metrics analysis engine 326 or others),prediction engine 416, or others. In some embodiments, one or moreprediction engines 416 may be part of or otherwise associated withconsumption control engine 412.

One or more of the engines in resource consumption control computer 406may communicate with one or more resource model repositories, such asresource model repository 418. Each resource model repository mayinclude one or more resource models, such as resource models 420. Eachof the one or more resource models may be associated with a resourcethat was, is, or is predicted to be available for consumption. One ormore of the engines in resource consumption control computer 406 maycommunicate with one or more performance model repositories, such asperformance model repository 422. Each performance model repository mayinclude one or more performance models, such as performance models 424.Each of the one or more performance models may be associated one or moreagent characteristics or conditions and may be employed to predict agentperformance for one or more agents associated with the one or more agentcharacteristics or conditions. In some embodiments, system 400 mayfacilitate comparing performance feedback from one or more agents 402with one or more outputs of one or more performance models 424 tocalibrate resource consumption control system 400 and improveperformance criterion.

In one or more of the various embodiments, an agent may provide arequest for a control session to resource consumption control computer406. In other embodiments, a supervisory or peer entity (for example, amaster controller, coach, teammate, team manager, or others) may providethe request on behalf of the agent. In some of the various embodiments,the request may include control session initialization information, suchas one or more of the following: identification information of theagent; identification information of an entity that employs or hosts theagent (for example, an assembly line, vehicle, school, sports club, orothers), associated agents (for example, peers, teammates, or others),or associated control sessions; contact information of the agent;characteristic information associated with the agent; preferred date ortime information for launching or concluding the control session; orothers. In some embodiments, the agent may enter the control sessioninitialization information in one or more fields in a user interfaceprovided by a web page, application, or others. In some embodiments, theuser interface may be arranged to provide the entered control sessioninitialization information to resource consumption control computer 406based on one or more actions of the agent, such as selecting a submitbutton on the web page or application.

In one or more of the various embodiments, resource consumption controlcomputer 406 may assign one or more performance monitors to therequested control session arbitrarily or based on the initializationinformation, such as one or more portions of the initializationinformation, demographics information (e.g., census information,information obtained in prior or concurrent control sessions, orothers), or others. For example, each performance monitor may beassociated with a logical territory (e.g., type of activity associatedwith the requested control sessions, type of entities, type of campaignagents, control session objectives, or others), geographical territory(e.g., postal codes or others), or others and may be assigned controlsessions for entities or agents associated with the performancemonitor's logical or geographic territory. In some of the variousembodiments, the one or more assigned performance monitors may take oneor more actions on behalf of the agent, such as providing one or moreportions of the request, providing one or more portions of theinitialization information, associating the agent with one or more otheragents, associating the agent with the entity, or others.

In one or more of the various embodiments, resource consumption controlcomputer 406 may obtain additional initialization information based onthe agent-provided information, such as demographics informationassociated with the entity, demographics information associated with ageographic region around the entity, or others. In some of the variousembodiments, resource consumption control computer 406 may contact theagent or entity to obtain additional control session initializationinformation. In other embodiments, the user interface may be arranged tocollect each portion of the initialization information to facilitatestarting initialization of a control session directly from the userinterface.

In one or more of the various embodiments, initialization informationmay include agent characteristics associated with the agent. In some ofthe various embodiments, the agent characteristics may include valuesfor various parameters associated with the agent, such as agent type(for example, internal combustion engine, Stirling engine, male, female,or others), age, size (for example, displacement, number of cylinders,height, or others), weight, material makeup (for example, quantities orpercentages of one or more elements, such as aluminum, iron, body fat,or others), activity rating (for example, a value selected on apredetermined scale such as between 0 and 10, 0 and 1, 1 and 2, orothers that indicates an amount of use above rest per predeterminedperiod, such as a duration or intensity of the use, or others),lifestyle rating (for example, a value selected on a predetermined scalesuch as between 0 and 10, 0 and 1, 1 and 2, or others that indicates howphysically active the agent is during the agent's time outside of theactivities measured by the activity rating, such as the agent'sprofessional time and non-training leisure time or others), impairmentstatus (for example, one or more values that indicate whether the agentis damaged or injured, the seriousness of the damage or injury, orothers), objectives (for example, performance improvement, performancemaintenance, weight loss, weight gain, weight maintenance, growth,development, or others), resource intake frequency (for example, anaverage number of times per day or other predetermined period that afuel tank is refilled, the number of meals or snacks per day, orothers), resource intake type (for example, standard complete tankrefill, pre-activity complete tank refill, post-activity complete tankrefill, standard partial tank refill, pre-activity partial tank refill,post-activity partial tank refill standard meal, pre-workout meal,post-workout meal, standard snack, pre-workout snack, post-workoutsnack, or others), or others. In some embodiments, one or more portionsof the agent characteristics information may be obtained from orgenerated by one or more sensors or sensor interfaces (for example, oneor more cameras 240, video interfaces 242, sensor interfaces 262, orothers) associated with the agent (for example, one or more sensors orsensor interfaces that are included in client computer 200, that are incommunication with client computer 200, or others). In some embodiments,one or more portions of the agent characteristics information (forexample, one or more unknown or estimated portions of the agentcharacteristics information, such as weight, material makeup, activityrating, lifestyle rating, or others) may be accumulated over apredetermined period prior to launching a control session. In someembodiments, resource consumption control computer 406 may generate oneor more agent data objects associated with the agent based on the agentcharacteristics information, such as one or more tables having recordsand attributes with associated fields that contain values that define,represent, or include one or more portions of the agent characteristicsinformation.

In one or more of the various embodiments, resource consumption controlcomputer 406 may evaluate one or more conditions of one or morecandidate performance models based on one or more portions of the agentcharacteristics information included in the one or more agent dataobjects associated with the agent. In some of the various embodiments,each candidate performance model may be associated with one or moreagent characteristics or conditions, such as one or more ranges,thresholds, or others for one or more parameters associated with agentcharacteristics. In some embodiments, resource consumption controlcomputer 406 may compare one or more values of one or more parameters inthe agent characteristics information to one or more condition values.In some embodiments, if the one or more evaluated conditions andportions of the agent characteristics information fail to match (forexample, the one or more parameter values failing to fall within anacceptable range defined by the one or more condition values), anotherone or more candidate performance models may be evaluated. In someembodiments, if the one or more evaluated conditions and portions of theagent characteristics information match, the one or more candidateperformance models may be selected.

In one or more of the various embodiments, one or more consumptioncontrol engines 412, prediction engines 416, or others may traverse theone or more selected performance models to generate a predicted energyconsumption needs amount for the agent based on one or more portions ofthe agent characteristics information. In some of the variousembodiments, one or more consumption control engines 412, predictionengines 416, or others may execute one or more portions of the one ormore selected performance models or one or more sub-models within theone or more selected performance models to generate one or morepredicted values (for example, one or more predicted baseline energyexpenditure rates, predicted interval energy expenditure amounts, orothers) based on one or more portions of the agent characteristicsinformation. In some embodiments, the one or more consumption controlengines 412, prediction engines 416, or others may input one or moreportions of the predicted values into one or more other portions of theone or more selected performance models or one or more other sub-modelswithin the one or more selected performance models to generate one ormore other predicted values (for example, another one or more predictedbaseline energy expenditure rates, predicted interval energy expenditureamounts, or others). In some embodiments, each output may be employed asan input to generate another output, and this process may iterativelycontinue, until the one or more selected performance models generate thepredicted energy consumption needs for the agent.

In one or more of the various embodiments, one or more consumptioncontrol engines 412, prediction engines 416, or others may execute oneor more portions of the one or more selected performance models or oneor more sub-models within the one or more selected performance models togenerate a predicted baseline energy expenditure rate based on one ormore portions of the agent characteristics information. In some of thevarious embodiments, the predicted baseline energy expenditure rate mayrepresent or include an amount of energy that the agent is predicted touse over a predetermined period of time at rest (for example, basalmetabolic rate (BMR) or others), such as Joules per minute that theagent uses at idle, calories that the agent uses daily at rest, orothers. In some embodiments, the one or more model portions orsub-models employed to generate the predicted baseline energyexpenditure rate may be selected based on one or more portions of theagent characteristics information, such as agent type (for example,internal combustion engine, Stirling engine, male gender, female gender,or others), weight, known material makeup (for example, quantities orpercentages of one or more elements, such as aluminum, iron, body fat,or others), age, size (for example, displacement, number of cylinders,height, or others), estimated material makeup, activity rating (forexample, a value selected on a predetermined scale such as between 0 and1 that indicates an amount of use above rest per predetermined period,such as a duration or intensity of the use, or others), or others. Inother embodiments, the one or more selected-model portions or sub-modelsmay be selected arbitrarily or may be selected for all agents. In someembodiments, one or more portions of agent characteristics informationemployed to select the one or more selected-model portions or sub-modelsmay also be input into the one or more selected portions or sub-modelsto generate the predicted baseline energy expenditure rate.

In one or more of the various embodiments, one or more consumptioncontrol engines 412, prediction engines 416, or others may execute oneor more portions of the one or more selected performance models or oneor more sub-models within the one or more selected performance models togenerate a predicted interval energy expenditure amount based on thepredicted baseline energy expenditure rate and one or more portions ofthe agent characteristics information. In some of the variousembodiments, the predicted interval energy expenditure amount mayrepresent or include an amount of energy that the agent is predicted touse over a predetermined interval when engaging in the typicalactivities and lifestyle of the agent (for example, an average amount ofenergy that the agent is predicted to use per interval), such as Joulesthat the agent uses per hour in a typical week (or average Joules thatthe agent uses per week), calories that the agent uses per day in atypical day (or average calories that the agent uses per day), orothers). In some embodiments, the predicted interval energy expenditureamount may include an adjustment to the predicted baseline energyexpenditure rate based on one or more portions of the agentcharacteristics information, such as activity rating (for example, avalue selected on a predetermined scale such as a value between zero andone that indicates an amount of use above rest per predetermined period,such as a duration or intensity of the use, or others), lifestyle rating(for example, a value selected on a predetermined scale such as between0 and 1 that indicates how physically active the agent is during theagent's time outside of the activities measured by the activity rating,such as the agent's professional time and non-training leisure time orothers), impairment status (for example, one or more values thatindicate whether the agent is damaged or injured, the seriousness of thedamage or injury, or others), or others. In some embodiments, the one ormore portions or sub-models employed to generate the predicted intervalenergy expenditure amount may be selected based on one or more portionsof the agent characteristics information, such as agent type (forexample, internal combustion engine, Stirling engine, male gender,female gender, or others), age, or others. In other embodiments, the oneor more selected-model portions or sub-models may be selectedarbitrarily or may be selected for all agents. In some embodiments, oneor more portions of agent characteristics information employed to selectthe one or more selected-model portions or sub-models may also be inputinto the one or more selected portions or sub-models to generate thepredicted interval energy expenditure amount.

In one or more of the various embodiments, one or more consumptioncontrol engines 412, prediction engines 416, or others may execute oneor more portions of the one or more selected performance models or oneor more sub-models within the one or more selected performance models togenerate a predicted energy consumption needs amount based on thepredicted interval energy expenditure and one or more portions of theagent characteristics information. In some of the various embodiments,the predicted energy consumption needs amount may represent or includean amount of energy that, if consumed by the agent when engaged in thetypical activities and lifestyle of the agent, is predicted to result inthe agent achieving one or more of the agent's objectives (for example,performance improvement, performance maintenance, weight loss, weightgain, weight maintenance, growth, development, or others). In someembodiments, the one or more model portions or sub-models employed togenerate the predicted energy consumption needs amount may be selectedbased on one or more portions of the agent characteristics information,such as one or more agent objectives (for example, rate or amount ofperformance improvement, performance maintenance, rate or amount ofweight loss, rate or amount of weight gain, weight maintenance, rate oramount of growth, rate or amount of development, or others), activityrating (for example, a value selected on a predetermined scale such as avalue between 0 and 1 that indicates an amount of use above rest perpredetermined period, such as a duration or intensity of the use, orothers), or others. In some embodiments, one or more portions of agentcharacteristics information employed to select the one or moreselected-model portions or sub-models may also be input into the one ormore selected portions or sub-models to generate the predicted energyconsumption needs amount.

In one or more of the various embodiments, the one or moreselected-model portions or sub-models may have associated outputthresholds that limit the maximum value or minimum value of thepredicted energy consumption needs amount based on the predictedbaseline energy expenditure rate, the predicted interval energyexpenditure amount, one or more portions of the agent characteristicsinformation (for example, activity rating, impairment status, or others)to, for example, prevent the agent from incurring damage based onconsumption of insufficient or excessive amounts of resources. In otherembodiments, one or more warnings may be provided to the agent or agraphical user interface based on the value of the predicted energyconsumption needs amount being above or below the one or more outputthresholds to, for example, provide an alert that indicates an elevatedrisk that the agent may incur damage based on consumption ofinsufficient or excessive amounts of resources. For example, a lowerthreshold for the predicted energy consumption needs amount may includethe predicted baseline energy expenditure rate, the predicted intervalenergy expenditure amount if the agent has an impairment (for example,performance-impeding damage, injury, illness, or others), apredetermined amount of energy (for example, 5 kilojoules (kJ), 1,200calories, or others), or others. As another example, an upper thresholdfor the predicted energy consumption needs amount may include apredetermined amount of energy (for example, 4 kJ, 1,000 calories, orothers) over the predicted interval energy expenditure amount, apredetermined amount of energy (for example, 2 kJ, 500 calories, orothers) over the predicted interval energy expenditure amount if theagent's activity rating is less than a predetermined threshold, orothers.

In one or more of the various embodiments, one or more consumptioncontrol engines 412, prediction engines 416, or others may execute oneor more portions of the one or more selected performance models or oneor more sub-models within the one or more selected performance models totransform the predicted energy consumption needs amount into resourcedistribution information. In some of the various embodiments, thetransformation may include transforming the predicted energy consumptionneeds amount into specific-units needs amounts for multiple resourcetypes, transforming the specific-units needs amounts intonormalized-units needs amounts for the multiple resource types, andtransforming the normalized-units needs amounts into resourcedistribution information. In some embodiments, specific-units needsamounts may include amounts that are specified in units that arespecific to particular resources, such as ounces, gallons, grams, orothers. In some embodiments, normalized-units needs amounts may includeamounts that are specified in units that are generic across allresources, such as servings or others (for example, one serving ofprotein may be described as corresponding to the volume of two standard52-card decks of playing cards or others). In some embodiments,specific-units needs amounts may not be converted directly intonormalized-units needs because a normalized-unit of one resource mayinclude one or more portions of a normalized-unit of another one or moreother resources (for example, a serving of gasoline may include one ormore fuel additives, a serving of fat may include protein, or others).Accordingly, in some embodiments, specific-units needs amounts for oneor more resource types may be transformed into normalized-units needsamounts for the one or more resource types based on specific-units needsamounts for the one or more resource types and normalized-units needsamounts for one or more other resource types.

In one or more of the various embodiments, one or more consumptioncontrol engines 412, prediction engines 416, or others may execute oneor more portions of the one or more selected performance models or oneor more sub-models within the one or more selected performance models togenerate specific-units needs amounts for multiple resource types (forexample, one or more gasolines, fuel stabilizers, oil additives,proteins, carbohydrates, fats, vegetables, or others) based on thepredicted energy consumption needs amount and one or more portions ofthe agent characteristics information. In some of the variousembodiments, the specific-units needs amounts for the multiple resourcetypes may be independently or discretely generated for each interval(for example, another set of specific-units needs amounts for themultiple resource types may be generated for each interval or others).In other embodiments, the specific-units needs amounts for the multipleresource types may be employed for each interval in one or more periods(for example, a set of specific-units needs amounts for the multipleresource types may be generated for each period, with each interval inthe period being assigned the same specific-units needs amounts for themultiple resource types, or others). In some embodiments, the one ormore portions or sub-models employed to generate the specific-unitsneeds amounts may be selected based on one or more portions of the agentcharacteristics information, such as agent age, material makeup (forexample, known or estimated quantities or percentages of one or moreelements, such as aluminum, iron, body fat, or others), activity rating(for example, a value selected on a predetermined scale such as a valuebetween zero and one that indicates an amount of use above rest perpredetermined period, such as a duration or intensity of the use, orothers), impairment status (for example, one or more values thatindicate whether the agent is damaged or injured, the seriousness of thedamage or injury, or others), objectives (for example, performanceimprovement, performance maintenance, weight loss, weight gain, weightmaintenance, growth, development, or others), or others. In otherembodiments, the one or more selected-model portions or sub-models maybe selected arbitrarily or may be selected for all agents. In someembodiments, one or more portions of agent characteristics informationemployed to select the one or more selected-model portions or sub-modelsmay also be input into the one or more selected portions or sub-modelsto generate the predicted baseline energy expenditure rate. In someembodiments, one or more specific-units needs amounts may be overriddenby one or more other components in system 400, such as agent 402,performance monitor 404, or others.

In one or more of the various embodiments, one or more consumptioncontrol engines 412, prediction engines 416, or others may execute oneor more portions of the one or more selected performance models or oneor more sub-models within the one or more selected performance models togenerate normalized-units needs amounts for each of the multipleresource types (for example, one or more gasolines, fuel stabilizers,oil additives, proteins, carbohydrates, fats, vegetables, or others)based on the respective specific-units needs amounts for the multipleresource types and the normalized-units needs amounts for one or moreother resource types. In some of the various embodiments, a temporarynormalized-units needs amount for one or more resource types may begenerated based on one or more portions of the agent characteristicsinformation, the predicted interval energy expenditure, the predictedenergy consumption needs amount, or others. In some embodiments, apredicted normalized-units needs amount for one or more resource typesmay be generated based on the temporary normalized-units needs amountfor one or more other resource types, one or more portions of the agentcharacteristics information, the predicted interval energy expenditure,the predicted energy consumption needs amount, or others. In otherembodiments, the normalized-units needs amount for one or more firstresource types (for example, vegetables or others) may be generatedbased on one or more portions of the agent characteristics information(for example, activity rating or others), the predicted interval energyexpenditure, the predicted energy consumption needs amount, or others.In some embodiments, the normalized-units needs amount for one or moreadditional resource types may be generated based on one or more portionsof the agent characteristics information (for example, age, weight,material makeup, impairment status, amount of objective weight loss orgain per predetermined period, or others), the normalized-units needsamount for the one or more first resource types, the normalized-unitsneeds amounts for one or more other additional resource types, thepredicted energy consumption needs amount, or others. In someembodiments, the normalized-units needs amounts for the multipleresource types may be independently or discretely generated for eachinterval (for example, another set of normalized-units needs amounts forthe multiple resource types may be generated for each interval orothers). In other embodiments, the normalized-units needs amounts forthe multiple resource types may be employed for each interval in one ormore periods (for example, a set of normalized-units needs amounts forthe multiple resource types may be generated for each period, with eachinterval in the period being assigned the same normalized-units needsamounts for the multiple resource types, or others).

In one or more of the various embodiments, the predicted interval energyexpenditure may be compared to the predicted energy consumption needsamount, and the one or more model portions or sub-models used togenerate the normalized-units needs for the one or more first resourcetypes may be selected based on the comparison and one or more portionsof the agent characteristics information (for example, activity rating).In other embodiments, the normalized-units needs amounts for eachresource type may be generated independent of the normalized-units needsamounts for each other resource type. In some of the variousembodiments, the one or more model portions or sub-models used togenerate the normalized-units needs for the one or more additionalresource types may be selected based on one or more portions of theagent characteristics information (for example, age, weight, materialmakeup, impairment status, objectives, or others).

In other embodiments, the one or more selected-model portions orsub-models employed to generate the normalized-units needs amounts forthe one or more first or additional resource types may be selectedarbitrarily or may be selected for all agents. In some embodiments, oneor more portions of agent characteristics information employed to selectthe one or more selected-model portions or sub-models may also be inputinto the one or more selected portions or sub-models to generate thenormalized-units needs amounts for the one or more first or additionalresource types.

In one or more of the various embodiments, one or more consumptioncontrol engines 412, prediction engines 416, or others may execute oneor more portions of the one or more selected performance models or oneor more sub-models within the one or more selected performance models togenerate resource distribution information based on the normalized-unitsneeds amounts for the multiple resource types, one or more portions ofthe agent characteristics information, or others. In some of the variousembodiments, one or more portions of the agent characteristicsinformation used to generate the resource distribution information mayinclude weight, resource intake frequency (for example, an averagenumber of times per day or other predetermined period that a fuel tankis refilled, the number of meals or snacks per day, or others), resourceintake type (for example, standard complete tank refill, pre-activitycomplete tank refill, post-activity complete tank refill, standardpartial tank refill, pre-activity partial tank refill, post-activitypartial tank refill standard meal, pre-workout meal, post-workout meal,standard snack, pre-workout snack, post-workout snack, or others), orothers. In some embodiments, the resource distribution information mayrepresent or include the normalized-units amounts for the multipleresources that the agent should intake during each resource intake. Insome embodiments, the normalized-units needs amounts for each resourcetype may be evenly distributed among the expected number of resourceintakes per predetermined duration (for example, the resource intakefrequency may represent or include an expected number of resourceintakes for each resource intake type per day or another predeterminedtime range). For example, the normalized-units needs amount for proteinmay be divided by the expected number of meals per day to generate adistributed normalized-units meals needs amount for protein.

In other embodiments, the normalized-units needs amounts for eachresource type may be distributed more heavily among the expected numberof resource intakes of one or more resource intake types (for example,standard complete tank refills, pre-activity complete tank refills,standard meals, pre-workout meals, or others) than the expected numberof resource intakes of another one or more resource intake types (forexample, standard partial rank refills, pre-activity partial tankrefills, standard snacks, pre-workout snacks, or others). For example,the normalized-units needs amount for protein may be weighted (forexample, multiplied by 0.75 or others) and may be divided by theexpected number of meals per day to generate the distributednormalized-units meals needs amount for protein, and thenormalized-units needs amount for protein may be less heavily weighted(for example, multiplied by 0.25 or others) and may be divided by theexpected number of snacks per day to generate a distributednormalized-units snacks needs amount for protein. In some embodiments,the resource distribution information may additionally or alternativelyrepresent or include the specific-units needs distributed among theexpected number of resource intakes. In some embodiments, the resourceintake frequency or resource intake type may have predetermined defaultvalues or may be set by the agent, the performance monitor, or others.In some embodiments, the one or more model portions or sub-models usedto generate the resource distribution information may be selected basedon one or more portions of the agent characteristics information (forexample, resource intake frequency, resource intake type, or others). Inother embodiments, the one or more selected-model portions or sub-modelsemployed to generate the normalized-units needs amounts for the one ormore first or additional resource types may be selected arbitrarily ormay be selected for all agents. In some embodiments, one or moreportions of agent characteristics information employed to select the oneor more selected-model portions or sub-models may also be input into theone or more selected portions or sub-models to generate the resourcedistribution information.

In one or more of the various embodiments, one or more resourceconsumption control engines 412 may provide resource consumption controlinstructions to the agent based on the resource distributioninformation. In some of the various embodiments, the resourceconsumption control instructions may be provided to the agent in bulk,such as multiple instructions associated with multiple intakes beingprovided at once. In other embodiments, the resource consumption controlinstructions may be provided to the agent for each intake (for example,at a time corresponding to another time at which an intake is expectedto occur, such as a predetermined amount of time before an event oraction that is expected to provide a sufficient duration to process theinstructions and take action in accordance with the instructions such asintake resources a predetermined amount of time before the event oraction, a predetermined amount of time after an event or action that isexpected to provide a sufficient duration to process and take action inaccordance with the instruction such as intake resources a predeterminedamount of time after the event or action terminates, or others). In someembodiments, the resource consumption control instructions may be pushedto the agent. In other embodiments, the agent may pull the resourceconsumption control instructions from one or more resource consumptioncontrol engines 412. In some embodiments, the resource consumptioncontrol instructions may represent or include the resource distributioninformation associated with each intake, presented in one or more ofspecific units, normalized units, or others.

In one or more of the various embodiments, one or more networkcomputers, such as one or more network computers 300 (for example, oneor more resource consumption control computers 406, consumption controlengines 324, consumption control engines 412, or others), may controlone or more agents 402 based on the one or more resource consumptioninstructions. In some of the various embodiments, controlling one ormore agents 402 may include providing the resource consumptioninstructions, thereby facilitating causing one or more client computers,such as one or more client computers 200 (for example, one or moreclient computers 102-105 or others), associated with or included in oneor more agents 402 to perform one or more actions in accordance with theone or more resource consumption instructions, such as one or more ofthe following: causing one or more agents 402 or one or more componentsof one or more agents 402 to intake or consume one or more resources,amounts of resources, resource recipes, resource types, or others;causing one or more projectors 246, displays 250, audio interfaces 256,haptic interfaces 264, or others to provide one or more outputs (forexample, displays, audio notifications, haptic notifications, or others)of one or more instructions, recommendations, interface components,alerts, or others (for example, when one or more applications 220 arenext launched, downloaded, opened, or otherwise engaged, when the one ormore resource instructions are obtained by client computer 200, orothers); or others. In some embodiments, the one or more networkcomputers may control one or more agents 402 by providing one or moreresource consumption control instructions that include values or recipeamounts presented in normalized units, thereby facilitating reducinglikelihood that the amount of agent consumption diverges from thepredicted energy consumption needs amounts, at least in comparison toproviding instructions that include values or recipe amounts presentedonly in specific units.

In one or more of the various embodiments, one or more consumptioncontrol engines 412, metrics analysis engines 414, or others may obtainmetrics associated with the control session for the agent. In someembodiments, the metrics may represent or include resource consumptioninformation (for example, amounts of resource types consumed in specificunits, normalized units, or others), objective performance information(for example, rating indication of how well the agent performed one ormore activities, how the agent felt during or after one or moreactivities, weight of the agent, or others), or others. In someembodiments, one or more client computers 200 may monitor one or moreactions or provide one or more of the metrics based on inputs in one ormore user interface components of a user interface of the agent. In someembodiments, one or more portions of the metrics may be obtained from orgenerated by one or more sensors or sensor interfaces (for example, oneor more cameras 240, video interfaces 242, sensor interfaces 262, orothers) associated with the agent (for example, one or more sensors orsensor interfaces that are included in client computer 200, that are incommunication with client computer 200, or others). In some embodiments,the metrics may be obtained for multiple qualifying periods, such as twoor more back-to-back qualifying periods or others. In some embodiments,a qualifying period may be defined as a set of a predetermined number ofback-to-back intervals (for example, 4-7 back-to-back days or others) inwhich the number of complete intervals meets or exceeds a minimumthreshold (for example, four or more complete intervals or others). Insome embodiments, a complete interval may be defined as an interval forwhich resource consumption information has been obtained for each intakeduring the interval (for example, discretely, in total, or others) andfor which objective performance information has been obtained for theinterval. In some embodiments, a qualifying period may include one ormore incomplete intervals (for example, an interval for which resourceconsumption information has not been obtained for one or more intakesduring the interval or for which objective performance information hasnot been obtained for the interval) between two complete intervals inthe qualifying period.

In one or more of the various embodiments, the resource consumptioninformation may include reported amounts of specific units, normalizedunits, or others for each consumed resource type. In some of the variousembodiments, the one or more network computers may permit the obtainedmetrics to include combinations of specific units, normalized units, orothers based on whichever is more convenient for the agent toselectively shift the transformation process to the one or more networkcomputers, thereby facilitating increasing the likelihood that orfrequency at which the agent records resource consumption information(for example, decreasing the processing expense or time required for theagent to record resource consumption information and, thus, increasingthe recording rate of the agent), which may increase the number of datapoints available for analysis and, thus, may increase accuracy of modeloutputs, information provided to performance monitors, instructionsprovided to agents, or others. In some embodiments, the reported amountsof specific or normalized units for each consumed resource type mayindicate the reported amounts of specific or normalized units for eachconsumed resource type over the entirety of the complete intervals inthe qualifying periods, over the entirety of the complete intervals ineach qualifying period, over each complete interval, over each intakesession in each complete interval, or others. In some embodiments,interval average amounts of specific or normalized units for eachconsumed resource type may be generated based on the reported amounts ofspecific or normalized units for each consumed resource type and thenumber of complete intervals. For example, the reported amounts ofspecific or normalized units for each consumed resource type over theentirety of the complete intervals in the qualifying periods may bedivided by the total number of complete intervals in the qualifyingperiods to generate the reported average interval amounts of specific ornormalized units for each consumed resource type.

In one or more of the various embodiments, one or more metrics analysisengines 414 may execute one or more models to transform the resourceconsumption information into total reported interval average amounts ofnormalized units for each consumed resource type based on thespecific-units needs amounts for each consumed resource type and thenormalized-units needs amounts for each consumed resource type. In someof the various embodiments, the reported interval average amounts ofspecific units for a consumed resource type may be divided by a ratio ofthe specific-units needs amount for the consumed resource type to thenormalized-units needs amount for the consumed resource type, and theresult may be added to the reported interval average amounts ofnormalized units for the consumed resource type to generate the totalreported interval average amounts of normalized units for the consumedresource type. In some embodiments, the ratio may be based on an averageof the specific-units needs amounts for the consumed resource type foreach interval in the period and an average of the normalized-units needsamounts for the consumed resource type for each interval in the period(for example, the ratio may represent a ratio of a per-interval averageof the specific-units needs amounts for the period and a per-intervalaverage of the normalized-units needs amounts for the period). In otherembodiments, the reported interval amounts of normalized units for theconsumed resource type may be obtained for each interval by employingthe ratio based on the specific-units and normalized-units needs amountsfor each interval and subsequently averaged to generate the totalreported interval average amounts of normalized units for the consumedresource type. In some embodiments, the total reported interval averageamounts of normalized units may be generated for each consumed resourcetype. In other embodiments, the reported interval average amounts ofnormalized units for each consumed resource type may be transformed intointerval average amounts of specific units for each consumed resourcetype (for example, multiplying the reported interval average amounts ofnormalized units by conversion factors associated with the consumedresource types, such as an average number of grams per protein servingor others), and the reported interval average amounts of specific unitsfor each consumed resource type may be summed with the transformedinterval average amounts of specific units for the consumed resourcetype to generate the total reported interval average amounts of specificunits for the consumed resource type.

In one or more of the various embodiments, one or more metrics analysisengines 414, prediction engines 416, or others may execute one or moremodels to generate an average amount of energy consumed by the agent perinterval based on the total reported interval average amounts ofnormalized or specific units for the consumed resource types. In some ofthe various embodiments, the total reported interval average amounts ofnormalized units for a consumed resource type may be multiplied by aconversion factor associated with the consumed resource type (forexample, the total number of reportedly consumed carbohydrate servingsmultiplied by the average number of grams per serving of carbohydratesor others), and the result may be summed with the number of specificunits for the consumed resource type that are expected to be included inthe total reported interval average amounts of normalized units for eachother consumed resource type (for example, the number expected based onone or more resource models 420 in resource model repository 418, suchas resource model 700), thereby facilitating generating total reportedinterval average amounts of specific units for the consumed resourcetype. In other embodiments, the reported interval average amounts ofnormalized units for a consumed resource type may be multiplied by aconversion factor associated with the consumed resource type (forexample, the number of reportedly consumed carbohydrate servingsmultiplied by the average number of grams per serving of carbohydratesor others) and summed with the reported interval average amounts ofspecific units for the consumed resource type, and the result may besummed with the number of specific units for the consumed resource typethat are expected to be included in the total reported interval averageamounts of normalized units for each other consumed resource type,thereby facilitating generating total reported interval average amountsof specific units consumed for the consumed resource type. In someembodiments, the total reported interval average amounts of specificunits may be generated for each consumed resource type. In someembodiments, the total reported interval average amounts of specificunits for each of the consumed resource types may be multiplied by theexpected amount of energy in each specific unit of the consumed resourcetype to generate the reported amount of energy for each of the consumedresource types, and the reported amounts of energy of the consumedresource types may be summed to provide a total average amount ofreportedly consumed energy per interval. In some embodiments, the totalaverage amount of reportedly consumed energy may be generated for theentirety of the complete intervals in the qualifying periods, for theentirety of the complete intervals in each qualifying period, for eachcomplete interval, for each intake session in each complete interval, orothers and may subsequently be transformed into the total reportedlyconsumed amount of energy per interval. In some embodiments, the averageamount of reportedly consumed energy per interval may be divided by thepredicted interval energy expenditure amount to obtain a consumptionresult, and, optionally, the comparison result may be multiplied by 100to provide the consumption result as a percentage.

In one or more of the various embodiments, one or more metrics analysisengines 414, prediction engines 416, or others may execute one or moreportions of the one or more selected performance models or one or moresub-models within the one or more selected performance models togenerate an average expected objective performance per interval (forexample, an average expected weight loss per day, an average expectedweight gain per day, or others) based on the predicted interval energyexpenditure, the average amount of reportedly consumed energy perinterval or the consumption result, the number of complete intervals, orothers. In some of the various embodiments, the predicted intervalenergy expenditure may be subtracted from the average amount ofreportedly consumed energy per interval to generate an average energydivergence amount per interval. In other embodiments, the consumptionresult may be transformed back into the average amount of reportedlyconsumed energy per interval prior to generating the average energydivergence amount per interval. In some embodiments, the average energydivergence amount per interval may be transformed into an averageexpected objective performance per interval (as measured in the unitsemployed for the objective performance information, such as pounds,kilograms, or others). For example, the average energy divergence amountper interval may be measured in calories, and the average energydivergence amount per interval may be divided by 7,700 to transform theaverage energy divergence amount per interval into the average expectedobjective performance per interval as measured in kilograms.

In one or more of the various embodiments, one or more metrics analysisengines 414, prediction engines 416, or others may execute one or moreportions of the one or more selected performance models or one or moresub-models within the one or more selected performance models togenerate a total expected objective performance based on the averageexpected objective performance per interval, the phase in each period atwhich each complete interval occurs, or others. In some of the variousembodiments, an average complete interval may be generated for eachqualifying period based on the phase in each qualifying period at whicheach complete interval occurs. For example, for a qualifying period of aweek that employs intervals of days with the qualifying period startingon Monday, Monday may be interval one, Tuesday may be interval two,Wednesday may be interval three, Thursday may be interval four, Fridaymay be interval five, Saturday may be interval six, Sunday may beinterval seven, and, with complete intervals of Monday, Wednesday,Thursday, and Sunday, the average complete interval may be 3.75((1+3+4+7)/4=3.75). In some embodiments, the number of intervals betweenthe average complete interval of the most recent qualifying period andthe average complete interval of the first qualifying period may becounted, including one of the first or most recent average completeinterval. For example, when the most recent qualifying period is theweek immediately following the first qualifying period and both the mostrecent and the first qualifying periods have average complete intervalsof 3.75, the number of intervals between the average complete intervalsmay be 7. In some embodiments, the average expected objectiveperformance per interval may be multiplied by the number of intervalsbetween the average complete intervals to generate the total expectedobjective performance.

In one or more of the various embodiments, one or more metrics analysisengines 414, prediction engines 416, or others may execute one or moreportions of the one or more selected performance models or one or moresub-models within the one or more selected performance models togenerate a calibration amount based on the total expected objectiveperformance, the objective performance information for each qualifyingperiod, the number of intervals between the average complete intervals,or others. In some of the various embodiments, the objective performanceinformation for each complete interval in each qualifying period may beaveraged to generate an average objective performance for eachqualifying period. For example, the sum of the weight of the agent ateach complete interval in a qualifying period may be divided by thenumber of complete intervals in the qualifying period to generate theaverage objective performance for the qualifying period. In someembodiments, the average objective performance of the first qualifyingperiod may be subtracted from the average objective performance of themost recent qualifying period to generate an objective performancechange. In some embodiments, the total expected objective performancemay be subtracted from the objective performance change to generate anobjective performance divergence. In some embodiments, the objectiveperformance divergence may be divided by the number of intervals betweenthe average complete intervals to generate an average objectiveperformance divergence per interval. In some embodiments, the averageobjective performance divergence per interval may be transformed intothe calibration amount (as measured in the units employed for thepredicted interval energy expenditure amount, such as calories orothers). For example, the average objective performance divergence perinterval may be measured in kilograms, and the average objectiveperformance divergence per interval may be divided by 7,700 to generatethe calibration amount as measured in calories.

In one or more of the various embodiments, one or more consumptioncontrol engines 412, metrics analysis engines 414, prediction engines416, or others may generate modified resource consumption instructionsbased on the calibration amount, the predicted interval energyexpenditure amount, the agent characteristics information, or others. Insome of the various embodiments, the calibration amount may be summedwith one or more previous calibration amounts for the agent (forexample, up to a predetermined number of previous calibration amountsthat may have been generated employing one or more similar processes asdescribed with regard to the calibration amount based on qualifyingperiods that may be prior to the most recent qualifying period, such asthree of the immediately preceding calibration amounts or others), andthe sum may be divided by the number of calibration amounts beingevaluated (including both the one or more previous calibration amountsand the calibration amount) to generate an average calibration amountfor a trailing window defined by the number of previous calibrationamounts employed. In some embodiments, the average calibration amountmay be added to the predicted interval energy expenditure amount togenerate a modified predicted interval energy expenditure amount. Insome embodiments, modified resource distribution information may begenerated employing one or more similar processes as described withregard to the resource distribution information based on the modifiedpredicted interval energy expenditure amount instead of the predictedinterval energy expenditure amount. In some embodiments, one or moreresource consumption control engines 412 may provide modified resourceconsumption control instructions to the agent employing one or moresimilar processes as described with regard to the resource consumptioncontrol instructions based on the modified resource distributioninformation instead of the resource distribution information. In someembodiments, the processes described with regard to obtaining themetrics associated with the control session through providing themodified resource consumption control instructions may continue untilthe control session terminates. For example, one or more of thesecontinuing processes may be executed for each intake session, completeinterval, qualifying period, or others.

In one or more of the various embodiments, one or more networkcomputers, such as one or more network computers 300 (for example, oneor more resource consumption control computers 406, consumption controlengines 324, consumption control engines 412, or others), may controlone or more agents 402 based on the one or more modified resourceconsumption instructions. In some of the various embodiments,controlling one or more agents 402 may include providing the modifiedresource consumption instructions, thereby facilitating causing one ormore client computers, such as one or more client computers 200 (forexample, one or more client computers 102-105 or others) associated withor included in one or more agents 402 to perform one or more actions inaccordance with the one or more modified resource consumptioninstructions, such as one or more of the following: causing one or moreagents 402 or one or more components of one or more agents 402 to intakeor consume one or more resources, amounts of resources, resourcerecipes, resource types, or others; causing one or more projectors 246,displays 250, audio interfaces 256, haptic interfaces 264, or others toprovide one or more outputs (for example, displays, audio notifications,haptic notifications, or others) of one or more instructions,recommendations, interface components, alerts, or others (for example,when one or more applications 220 are next launched, downloaded, opened,or otherwise engaged, when the one or more modified resourceinstructions are obtained by client computer 200, or others); or others.

In one or more of the various embodiments, modifying the predictedinterval energy expenditure amount in a control session for an agent (orthe modified predicted interval energy expenditure amount if system 400has been calibrated one or more times in the control session for theagent) may facilitate improving one or more performance criteria, suchas increasing a performance criterion of correlation between theobjective performance of the agent and one or more outputs of one ormore models, model portions, sub-models, or others (for example, one ormore of the value of the correlation coefficient or the absolute valueof the correlation coefficient describing the direction or magnitude ofthe relationship between the objective performance of the agent and oneor more outputs of one or more models, model portions, sub-models, orothers, such as the predicted energy consumption needs amount, thespecific-units needs amount for one or more resource types, thenormalized-units needs amount for one or more resource types, theresource distribution information, the resource consumptioninstructions, or others). In some of the various embodiments, modifyingthe predicted interval energy expenditure amount in a control sessionfor an agent (or the modified predicted interval energy expenditureamount if system 400 has been calibrated one or more times in thecontrol session for the agent) by reducing the predicted interval energyexpenditure amount may reduce the magnitude of the one or more outputsand may increase the objective performance of the agent (for example,weight loss), thereby increasing the performance criterion of the valueof the correlation coefficient and the performance criterion of theabsolute value of the correlation coefficient (for example, bringing thevalue of the correlation coefficient closer to positive one). In otherembodiments, modifying the predicted interval energy expenditure amountby increasing the predicted interval energy expenditure amount mayincrease the magnitude of the one or more outputs and may increase theobjective performance of the agent (for example, weight gain), therebyincreasing the performance criterion of the value of the correlationcoefficient and the performance criterion of the absolute value of thecorrelation coefficient (for example, bringing the value of thecorrelation coefficient closer to positive one).

In one or more of the various embodiments, modifying the predictedinterval energy expenditure amount in a control session for an agent (orthe modified predicted interval energy expenditure amount if system 400has been calibrated one or more times in the control session for theagent) may improve the responsiveness, reliability, ease-of-use, orother characteristics of system 400. For example, the modification maycalibrate one or more components of system 400 to compensate for one ormore divergences in an agent's interpretation of the resourceconsumption instructions, the agent's recording of consumptioninformation, makeup anomalies or divergences associated with the agent(for example, manufacturing or genetic variances from an average agentof the agent type having the agent characteristics information), orothers, without requiring system 400 to engage in more computationallyexpensive modelling to identify and directly address the one or moredivergences. Accordingly, in some of the various embodiments, themodifications described with regard to system 400 based on thecomparison of the performance feedback with the output of one or moreperformance models may improve performance criterion while improving theefficiency of one or more agents 402, resource consumption controlcomputers 406, or other components in system 400.

In one or more of the various embodiments, the one or more performancemonitors assigned to the agent may monitor one or more of the agent'sactions, such as the agent's adherence to or divergence from theresource consumption control instructions, adherence to or divergencefrom the modified resource consumption control instructions, accuracy ofrecording resource consumption information, activities (for example,training, exercise, or others), impairment status, or others. In some ofthe various embodiments, resource consumption control computer 406 mayprovide a dashboard (for example, a dashboard employing a graphical userinterface provided in a web page, an application, or others) to adisplay of a performance monitor. In some embodiments, the dashboard mayinclude monitor information, such as raw data in the obtained metrics,simplified forms of the obtained metrics, supplemental informationgenerated based on the obtained metrics, or others. In some embodiments,the dashboard may include information associated with multiple agents.In some embodiments, the multiple agents may be agents that are assignedto the performance monitor, are associated with each other (for example,agents on the same sports team, agents within the same athletic programat a school, or others), have one or more portions of agentcharacteristics information in common (for example, same or similaragent type, same or similar age, same or similar weight, same or similarmaterial makeup, same or similar objectives, or others.

In some embodiments, the dashboard may display averages of monitorinformation associated with the multiple agents, one or more sub-groupswithin the multiple agents (for example, athletes of a particular genderor others), or others. In some embodiments, the dashboard may displaypercentages associated with the monitor information, such as the percentof agents in the multiple agents that are meeting their intake needs(for example, protein intake needs or others), engaging in intakesessions between particular hours, above or below their intake needs,engaging in their typical activities, or others. In some embodiments,the dashboard may display one or more flags when one or more portions ofthe monitor information associated with the agent diverge beyond athreshold from an expected value, from average monitor information ofthe multiple agents, from one or more historical trends associated withthe agent, or others. In some embodiments, the monitor information inthe dashboard may be updated in real time, such as when the informationis obtained from agent, when the information is processed by resourceconsumption control computer 406, or others. In other embodiments, themonitor information in the dashboard may be provided as a snapshot at apredetermined time, at the time at which the performance monitor logsinto the web page, application, or other element that acts as a gatewayto the dashboard, or others. In some embodiments, the dashboard mayobtain monitor metrics (for example, login time, login duration, time ofcommunications, or others) associated with one or more actions of theperformance monitor and may provide the obtained monitor metrics toresource consumption control computer 406. In some embodiments, resourceconsumption control computer 406 may adjust the predetermined time atwhich to provide the snapshot based on one or more averages of one ormore portions of the obtained monitor metrics. In some embodiments, thedashboard may provide one or more interface components (e.g., one ormore buttons, check boxes, radio buttons, text fields, or others) thatfacilitate the performance monitor providing feedback (for example,reporting one or more flags, divergences, percentages or others;providing instructions to remedy the reported information; or others) tothe agent, providing feedback to one or more entities that supervise theagent (for example, one or more coaches, trainers, or others),overriding one or more instructions to the agent, altering informationor inputs provided by the agent, creating one or more accountsassociated with the agent, one or more other actions described herein asbeing performed by the agent, or others. In some embodiments, theperformance monitor may be a specialist in one or more fields, such asone or more fields associated with the performance objectives of themultiple agents. In other embodiments, the performance monitor mayinclude a form of artificial intelligence, learning engines, neuralnetworks, or others that may employ one or more processes described withregard to the dashboard and, optionally, without a display.

In one or more of the various embodiments, one or more resourceclassification engines 408 may generate one or more resource dataobjects (for example, see FIG. 7) for each resource that an agent isexpected to consume or that the agent reports as having been consumedbased on one or more portions of resource characteristics informationassociated with the one or more resources. In some of the variousembodiments, an agent, an entity that supervises the agent, aperformance monitor, administrator, or others may communicate one ormore portions of the resource characteristics information over one ormore networks to one or more resource consumption control computers 406.In some embodiments, one or more portions of the resourcecharacteristics information may be provided via a user interface (forexample, a web page, application, or others) in various forms, such asemail, user interface (UI) notification, instant message, or others. Inother embodiments, one or more portions of the resource characteristicsinformation may be provided or generated by one or more sensors orsensor interfaces (for example, one or more cameras 240, videointerfaces 242, sensor interfaces 262, or others). In some embodiments,the resource characteristics information may include one or more energysources (for example, protein, carbohydrates, fats, or others) in termsof percentages (for example, a given amount of a resource may derivevarious percentages of its energy from different energy sources) orspecific units (for example, grams or others).

In one or more of the various embodiments, each resource may beclassified as being of a resource type. In some of the variousembodiments, each resource may have one or more characteristicsassociated with multiple resource types, and the resource may beclassified to one of the multiple resource types based on one or moremodels, model portions, sub-models, conditions, or others. In someembodiments, one or more conditions of the selected resource type may beevaluated based on one or more portions of the resource characteristicsinformation. In some embodiments, each resource type may have one ormore conditions that, if satisfied, indicate that the one or moreportions of the resource characteristics information may match with theresource type. In some embodiments, a condition may include that the oneor more portions of the resource characteristics information has notmatched with a higher-ranking resource type in a hierarchy of resourcetypes. For example, vegetables may have highest priority, carbohydratesmay have the second highest priority, proteins may have the thirdhighest priority, and fats may have the lowest priority. Accordingly, insome embodiments, a resource that satisfies conditions of multipleresource types may only match with the satisfied resource type that hasthe highest priority. In some embodiments, a condition may include mostof the percentage of the one or more energy sources of the resourcebeing the resource type (for example, the resource derives a greaterpercentage of its energy from the evaluated resource type than thepercentage of its energy derived from each other resource type). In someembodiments, a condition may include the highest specific units amountof the one or more energy sources of the resource being the resourcetype. In some embodiments, one or more of multiple conditions must besatisfied or others.

In one or more of the various embodiments, when a resource is classifiedas having a resource type, specific-units amounts for the resource maybe transformed into normalized-units amounts for the new resource basedon one or more portions of the resource characteristics information andthe associated resource type. In some of the various embodiments, anexpected number of normalized units per a predetermined number ofspecific units may be based on historical analysis provided by a thirdparty, average sample analysis, user input, or others. In someembodiments, the energy source information may be employed to generatethe number of normalized units per number of specific units. In otherembodiments, the number of normalized units per number of specific unitsmay be standard based on the average or expected number for the matchedresource type (for example, 25 grams of a resource that is a resourcetype of protein may equate to one serving of protein or others). In someembodiments, resource data object of the resource may be populated withthe numbers of normalized or specific units associated with theresource.

In one or more of the various embodiments, one or more resource recipeengines 410 may generate one or more resource recipes to be employed byone or more agents 402 based on one or more portions of resourcecharacteristic information in two or more resource data objectsassociated with two or more resources. In some of the variousembodiments, two or more resources may be evaluated to generate acompatibility score associated with the combination of the two or moreresources. In some embodiments, the resource and the other resource maybe compatible with each other if one or more portions of theirrespective resource characteristic information overlaps, matches,satisfies one or more conditions, fails to overlap, fails to match, orothers. In some embodiments, the degree of compatibility may bereflected in the score based on the amount of overlap, the quantities ofoverlaps, the number of conditions satisfied, or others. For example,the resource and the other resource may be compatible with each other iftheir respective resource characteristic information indicates that theymay both be breakfast foods, if only one of them takes a large amount oftime or effort to prepare, if they are of different resource types, iftheir combination correlates to a predicted high likelihood of agent orcontrol session success, or others. In some embodiments, compatibilitymay vary based on geographic region for which the recipe is intended. Insome embodiments, one or more configuration files, rules, customscripts, or others may define one or more thresholds for a minimumnumber of resources to include in a recipe based on one or more factors,such as whether the recipe is a snack or a meal recipe, the type ofsnack or meal (for example, standard, pre-workout, post-workout, quickpreparation, normal preparation, gourmet preparation, or others).

In one or more of the various embodiments, one or more portions of theresource characteristics information of each resource in the recipe maybe evaluated to select one or more resource consumption or intake phasesto associate with the recipe. In some of the various embodiments, eachphase associated with each of the resources in the recipe may beselected. In some embodiments, one or more phases associated with therecipe may vary based on geographic region, agent type, or others forwhich the recipe is intended. In some embodiments, one or more resourcerecipes may be generated based on one or more portions of the resourcecharacteristics information associated with the selected resources andbased on the one or more selected resource consumption phases. In someembodiments, generating the recipe may include generating or populatingone or more recipe data objects associated with the recipe. In someembodiments, the one or more recipe data objects may be generated orpopulated employing one or more similar processes as described withregard to one or more other data object generation or populationprocesses of system 400, one or more processes described with regard toone or more data structures described with regard to FIG. 6 or 7, orothers. In some embodiments, one or more attributes of the one or morerecipe data objects may indicate one or more restrictions (for example,allergies, dietary choices or restrictions, intake type, geographicalassociations, intake phases, or others).

In one or more of the various embodiments, the client computers, agents402, performance monitors 404, or others may have vertical access to oneor more agent repositories, the agent data objects in the one or moreagent repositories, information included in or associated with the agentdata objects, or others, thereby permitting access to authorizedinformation in one or more agent repositories associated with the one ormore control sessions that are associated with the client computers,agents 402, performance monitors 404, or others. In some of the variousembodiments, the client computers, agents 402, performance monitors 404,or others may lack horizontal access, thereby precluding accessing toinformation in one or more agent repositories associated with one ormore control sessions that are not associated with the client computers,agents 402, performance monitors 404, or others. Accordingly, in someembodiments, the client computers, agents 402, performance monitors 404,or others may track progress of the control sessions with which theclient computers, agents 402, performance monitors 404, or others aredirectly involved, yet information associated with the other controlsessions remains private or protected.

In contrast, in one or more of the various embodiments, one or moreengines in resource consumption control computer 406 or system 400 (forexample, one or more performance monitors, resource recipe engines 410,consumption control engines 412, metrics analysis engines 414,prediction engines 416, or others) may have both vertical and horizontalaccess to the one or more agent repositories, the agent data objects inthe one or more agent repositories, the information included in orassociated with the agent data objects, or others, thereby permittingaccess to information associated with each control session. In some ofthe various embodiments, metrics analysis engine 414 may analyze metricsassociated with multiple active or completed control sessions andprovide the results of the analysis to consumption control engine 412.In some embodiments, one or more metrics analysis engines 414 or othersmay classify or categorize one or more control sessions, agents 402, orothers based on evaluation of one or more data objects associated withthe one or more control sessions, agents 402, or others and may includethe classification or categorization in the results. In someembodiments, one or more of the services provided by metrics analysisengine 414 may be provided by one or more third-party services (forexample, one or more services available under the mark MIXPANEL orothers). Accordingly, in some embodiments, consumption control engine412 may predictively or responsively select one or more models, portionsof one or more models, sub-models in one or more models, strategies,tactics, or others to employ in one or more active or impending controlsessions based on the results provided by metrics analysis engine 414for one or more control sessions with similar characteristics to the oneor more active or impending control sessions (for example, one or morepredictions discovered by one or more prediction engines 416). In someembodiments, consumption control engine 412 may predictively orresponsively adjust or select one or more models, portions of one ormore models, sub-models in one or more models, strategies, tactics, orothers employed in one or more active or impending control sessionsbased on the results provided by metrics analysis engine 414 indicatingthat one or more models, portions of one or more models, sub-models inone or more models, strategies, tactics, or others was more effective inone or more other control sessions with similar characteristicsassociated with the one or more active or impending campaigns.Accordingly, in some embodiments, because system 400 may facilitatehorizontal and vertical analysis of control sessions, system 400 mayfacilitate analyzing or modifying control sessions more quickly thanclient computers, agents 402, performance monitors 404, specialists, orothers could otherwise do on their own with vertical access, therebyimproving security of agent information and effectiveness of controlsessions, such as decreasing time taken to execute a control session toachieve objectives or others. Moreover, in some embodiments, becauseconsumption control engine 412 may horizontally analyze tenants ofsystem 400, consumption control engine 412 may have and may apply one ormore rules to prevent competing control sessions (for example, two ormore control sessions that have conflicting objectives, instructions, orothers).

In one or more of the various embodiments, one or more selections,compositions, characteristics, or others of one or more initiation,launch, or execution processes or of one or more actions or otherprocesses described with regard to one or more performance monitors 404,resource consumption control computers 406, resource classificationengines 408, resource recipe engines 410, consumption control engines412, metrics analysis engines 414, prediction engines 416, or othercomponents of system 400 (for example: selecting or executing one ormore models, portions of one or more models, sub-models of one or moremodels, or others; one or more models, portions of one or more models,sub-models of one or more models themselves; selecting or generating oneor more parameters, variables, coefficients, constants, or othercomponents of one or more models, portions of one or more models,sub-models of one or more models; or others) may be based on one or moremachine learning models, linear regression models, heuristics models, orothers derived from relevant historical metrics for one or more othercontrol sessions. In some of the various embodiments, the relevanthistorical metrics may include information associated with one or moreother agents, geographical or logical territories, entities associatedwith agents, performance monitors, or others having one or more similarcharacteristics or one or more characteristics that correspond to one ormore characteristics of the control session, agent, agents associatedwith the agent, the entity associated with the agent, geographical orlogical territory associated with the agent, or others. In someembodiments, the one or more machine learning models, linear regressionmodels, heuristic models, or others may be employed to discover one ormore candidate selections or modifications that are predicted to improvesystem performance, agent performance, or other performance or to reducetime the control session may be expected to obtain one or moreobjectives.

In some embodiments, the one or more predictions may be based on orinclude one or more predictions discovered by prediction engine 416. Inother embodiments, one or more machine learning models, linearregression models, heuristics models, or others may be applied tohistorical metrics for one or more other control sessions associatedwith one or more data objects of one or more supervisory entities,agents 402, performance monitors 404, resource consumption controlcomputers 406, or others to provide one or more portions of thediscoveries. In some embodiments, a deep-learning artificial neuralnetwork may be trained using historical information to classify oridentify one or more features of the one or more selections,compositions, characteristics, or others that are predicted to haveincreased success rates (for example, increased likelihood of achievingone or more objectives, decreased expected time until one or moreobjectives may be achieved, increased wellness of the agent during orafter the control session, or others) based on one or more predictionsdiscovered by prediction engine 416.

For example, one or more discoveries may indicate that one or moreagents that have actual performances that diverge from the expectedperformances by amounts within one or more particular ranges or thathave one or more portions of the agent characteristics informationmatching one or more conditions or ranges of conditions, struggled orfailed to adhere to the resource consumption control instructions (forexample, struggle or failure to adhere to one or more intake schedules,to adhere to the instructed amounts of normalized-units to consume forone or more of the multiple resource types, or others), and, in someembodiments, one or more discoveries may indicate that adjusting theresource control instructions (for example: adjusting one or moreparticular aspects of the intake schedules, such as including additionalsnack intake sessions at particular times or others; adjusting therecommended or instructed resource recipes in a particular manner orothers; or others). Accordingly, in some embodiments, one or more agentsthat have one or more portions of the agent characteristics informationmatching the one or more conditions or ranges of conditions or that haveactual performances that diverge from the expected performances byamounts within the one or more particular ranges may be provided withthe adjusted resource control instructions (for example, transformingthe normalized-units needs amounts or modified normalized-units needsamounts into the adjusted resource control instructions based on one ormore adjusted portions of resource distribution information or others).As another example, one or more discoveries may indicate that presentingthe instructions in different formats, at different times, at differentphases, at different frequencies, or others may improve performance ofone or more agents that have one or more portions of the agentcharacteristics information matching the one or more conditions orranges of conditions or that have actual performances that diverge fromthe expected performances by amounts within the one or more particularranges. Accordingly, in some embodiments, one or morediscovery-identified characteristics of one or more performance monitors404, resource recipe engines 410, consumption control engines 416,metrics analysis engines 414, prediction engines 416, models, portionsof one or more models, sub-models of one or more models, processes,actions, or others may be modified based on the horizontal evaluation ofthe one or more obtained metrics and one or more correspondingpredictions discovered by prediction engine 416.

In one or more of the various embodiments, resource consumption controlcomputer 406 may obtain interaction metrics associated with the resourceconsumption control instructions or the dashboard provided to one ormore agents 402, performance monitors 404, or others. In some of thevarious embodiments, each interaction with the instructions, userinterface displaying the instructions, or the dashboard may be reportedto resource consumption control computer 406, and resource consumptioncontrol computer 406 may obtain interaction metrics associated with eachinteraction, such as date, time of day, frequency, duration, type,application used to view the instructions or dashboard, user-interfaceinteraction (for example, mouse clicks, types of clicks, mouse hovertime, opens, visits, refreshes, timing of actions, most-recent logintime, quantities of actions, or others), delay between providing theinstruction or dashboard and the interaction, or others. In someembodiments, resource consumption control computer 406 may adjust one ormore discovery-identified characteristics of one or more performancemonitors 404, resource recipe engines 410, consumption control engines416, metrics analysis engines 414, prediction engines 416, models,portions of one or more models, sub-models of one or more models,processes, actions, or others that are predicted to have increasedsuccess rates (for example, increased likelihood of achieving one ormore objectives, decreased expected time until one or more objectivesmay be achieved, increased wellness of the agent during or after thecontrol session, or others) based on one or more predictions discoveredby prediction engine 416 and the obtained interaction metrics.

In one or more of the various embodiments, one or more of the selectionsof one or more models, model portions, or sub-models described hereinmay be performed for each intake, interval, period, control session, orothers.

In one or more of the various embodiments, system 400 may increase theefficiency of control sessions, accuracy of performance models, qualityof results of control sessions, or others by one or more of improvingcommunication between the various components of each control session(for example, one or more agents, performance monitors, resourceconsumption control computers, or others), enabling oversight by anobjective entity (for example, one or more performance monitors orothers), calibrating control system 400, resource consumption controlcomputer 406, or others based on a comparison of performance feedbackwith the output of a performance model, or others. In some of thevarious embodiments, when one or more control session components (forexample, one or more agents, performance monitors, resource consumptioncontrol computers, or others) are offline from one or more networks, oneor more sending, forwarding, or other control session components insystem 400 (for example, one or more agents, performance monitors,resource consumption control computers, or others) may cache one or moreportions of one or more communications intended for the one or moreoffline campaign components. In some embodiments, the one or moresending, forwarding, or other control session components may detect thatthe one or more offline components went offline based on one or more oflosing one or more connections to one or more applications, engines, orothers in the one or more offline components, failing to obtain aresponse to one or more communications (e.g., one or more ACKcommunications or others), or others. In some embodiments, when the oneor more control session components come back online, the one or morerejoining components may notify one or more other control sessioncomponents. In some embodiments, the one or more sending, forwarding, orother control session components may detect the return of the one ormore rejoining components based on one or more notifications,reconnections to one or more engines in the one or more rejoiningcomponents, responses to one or more prior communications to the one ormore rejoining components, or others.

In one or more of the various embodiments, when the one or more controlsession components come back online, the one or more sending,forwarding, or other control session components in system 400 mayresynchronize the one or more rejoining control session components in asynching process. In some of the various embodiments, the synchingprocess may include auditing information in the one or more rejoiningcomponents to evaluate one or more states of the one or more rejoiningcomponents, whether one or more portions of information in the one ormore rejoining components is up to date, or others. In some embodiments,the audit may include obtaining a timestamp associated with amost-recent update of the one or more rejoining components. In someembodiments, the synching process may include evaluating the auditedinformation to produce one or more results that indicate whether one ormore states, information, or others in the one or more rejoiningcomponents are out of date. In some embodiments, the evaluation mayinclude comparing the timestamp to one or more timestamps associatedwith a most-recent update provided to one or more other control sessioncomponents. In some embodiments, the synching process may includeupdating information in the one or more rejoining components to reflectupdated information associated with the control session, such as stateinformation associated with the control session (e.g., the controlsession is being initialized, has launched, has concluded, or others) orothers.

In one or more of the various embodiments, system 400 may improveefficiency, responsiveness, reliability, or others by selectively makingvarious elements available offline. In some of the various embodiments,when an agent installs, loads, opens, or otherwise initializes adownloaded (or native) element, such as one or more applications orengines (for example, performance tracking engine 218, performancemonitor engine 222, or others), the downloaded element may perform acall to one or more data structures (for example, one or more tables orothers) in one or more repositories (for example, resource modelrepository 418, performance model repository 422, an agent data objectrepository, or others) to pull one or more data structures, portions ofone or more data structures, or others to populate one or more elementsemployed or provided by the downloaded element, such as one or moregraphical displays or others, and may store the pulled informationlocally at the agent. In some embodiments, the downloaded element mayprovide one or more input fields (for example, one or more input fieldsin a user interface, an API associated with one or more otherapplications or engines of the agent, or others) that facilitates theagent inputting information into the downloaded element. In someembodiments, the input information may be stored locally at the agent(in some embodiments, one or more portions of the input information mayalso be pushed to resource consumption control computer 406).Accordingly, in some embodiments, the downloaded element may beresponsive to agent actions both when the agent is connected to anetwork and when the network is unavailable to the agent (and,therefore, resource consumption control computer 406 is unavailable tothe agent).

In one or more of the various embodiments, the downloaded element mayhave one or more configuration files, rules, custom scripts, or othersthat selectively maintain one or more portions of the locally storedinput or pulled information, archive one or more portions of the locallystored input or pulled information, remove one or more portions of thelocally stored input or pulled information, or others based on one ormore characteristics of one or more portions of the locally stored inputor pulled information. In some embodiments, the one or moreconfiguration files, rules, custom scripts, or others may locallymaintain one or more portions of the local information untilpredetermined event (for example, a predetermined date or time isreached, the one or more portions of information have a predeterminedage, or others) based on one or more characteristics of the localinformation (for example, agent-settings or configuration information,administrator settings, information type, criticality of theinformation, time since last use of the information, frequency of use ofthe information, frequency of change to the information, likelihood ofre-use, or others). In some embodiments, when a predetermined eventassociated with one or more portions of local information occurs, theone or more configuration files, rules, custom scripts, or others mayarchive or remove the one or more portions of the local informationbased on one or more characteristics of the local information (forexample, agent-settings or configuration information, administratorsettings, information type, criticality of the information, time sincelast use of the information, frequency of use of the information,frequency of change to the information, likelihood of re-use, orothers). In some embodiments, the one or more configuration files,rules, custom scripts, or others may remove one or more portions oflocal information when the agent reconnects to the network and providesthe one or more portions of local information to resource consumptioncontrol computer 406. In some embodiments, the one or more configurationfiles, rules, custom scripts, or others may remove one or more portionsof local information when the agent reconnects to the network if theresource consumption control computer 406 informs the downloaded elementthat the one or more portions of local information are out of date or ifthe downloaded element obtains updated information that takes priorityover the one or more portions of local information (for example, updatedversions of information or others). Accordingly, system 400 mayfacilitate the downloaded element being responsive to agent actions whenoffline from the network and may facilitate preventing over fillinglocal storage or memory of the agent.

In one or more of the various embodiments, system 400 may improvecommunication reliability or coverage by employing one or morefailovers. In some of the various embodiments, the one or more failoversmay include multiple communication providers that may be dynamicallyemployed based on availability of the one or more other communicationproviders, such as multiple email providers (for example, SENDRID,ANDRIL, or others) or others. In some embodiments, the one or morefailovers may include multiple communication modalities that may bedynamically employed based on availability of the one or more othercommunication modalities, such as two or more of email, SMS (forexample, SMS provided by TOYO or others), user interface (UI)notification, instant message, or others. In some embodiments, one ormore configuration files, rules, custom scripts, or others may executelogic (for example, confirming that no ACK communications were receivedor others) to prevent duplicate actions when switching providers, modes,or others. In some embodiments, one or more pools of providers ormodalities may be provided, and a provider or modality may be selectedfrom the one or more pools based on one or more input conditions, on arotating basis, or others. Accordingly, system 400 facilitatesconducting control sessions over wide geographic regions with varyinglevels of network reliability or coverage by facilitating employingmulti-modal networks and dynamically changing modes of communicationfrom one mode to another based on availability of the communicationmodes. For example, a performance monitor may attempt to send acommunication to an agent through a performance monitor engine (forexample, performance monitor engine 222), and the performance monitorengine may dynamically select one or more modes of communication, one ormore providers, or others based on network availability associated withthe one or more modes of communication, one or more providers, orothers. In some embodiments, the performance monitor engine may provideone or more notifications (for example, a UI notification or others)that the performance monitor engine is attempting to dynamically selecta different mode of communication, provider, or others. In otherembodiments, the performance monitor engine may dynamically select thedifferent mode of communication, provider, or others without furthernotification.

In one or more of the various embodiments, one or more engines (forexample, one or more performance tracking engines 218, performancemonitor engines 222, resource classification engines 318 or 408,resource recipe generation engines 322 or 410, consumption controlengines 324 or 412, metrics analysis engines 326 or 414, predictionengines 416, or others) may employ one or more other engines to performone or more actions (for example, one or more of the actions describedwith regard to one or more components of system 400). In some of thevarious embodiments, employing another engine to perform an action mayinclude providing one or more instructions, portions of information (forexamples, one or more portions of one or more inputs to one or moremodels, model portions, sub-models, or others), signals, or others tothe other engine, thereby facilitating the other engine performing theaction and, optionally, obtaining one or more instructions, portions ofinformation, signals, or others from the other engine based on the otherengine performing the action. In some embodiments, employing anotherengine to perform an action may include providing one or moreinstructions, portions of information (for examples, one or moreportions of one or more inputs to one or more models, model portions,sub-models, or others), signals, or others to the other engine andobtaining one or more one or more instructions, portions of information,signals, or others from the other engine based on one or more actionsperformed by the other engine responsive to the one or more providedcommunications, thereby facilitating one or more of the engine or one ormore further engines performing the action based on one or more portionsof the one or more obtained communications from the other engine.

In one or more of the various embodiments, one or more engines (forexample, one or more performance tracking engines 218, performancemonitor engines 222, resource classification engines 318 or 408,resource recipe generation engines 322 or 410, consumption controlengines 324 or 412, metrics analysis engines 326 or 414, predictionengines 416, or others) may instantiate one or more other engines toperform one or more actions (for example, one or more of the actionsdescribed with regard to one or more components of system 400). In someof the various embodiments, instantiating another engine to perform anaction may include generating an instance of the other engine, therebyfacilitating the other engine performing the action. In someembodiments, instantiating another engine to perform an action mayinclude generating one or more representations of the other engine (forexample, setting one or more flags, variable values, or others to, forexample, indicate that the other engine is tasked with ensuring that theaction is performed or others). In some embodiments, instantiatinganother engine to perform an action may include employing the otherengine to perform the action.

FIG. 5 illustrates a logical representation of a portion of exampledecision model 500 that may be employed by one or more engines in system400 to execute one or more portions of one or more processes, actions,or others described with regard to the one or more engines. In one ormore of the various embodiments, a decision tree may be an example wayto implement decision model 500. In some of the various embodiments, oneor more performance monitors 404, resource classification engines 408,resource recipe engines 410, consumption control engines 412, metricsanalysis engines 414, prediction engines 416, or others may be arrangedto support models that have various shapes or structures. In someembodiments, the processes used by one or more engines may be adapted orvaried depending on the structure of a given model. In some embodiments,the particular shape or structure of a model may be shared with anengine to facilitate the engine selecting a protocol that may becompatible with the model. In some embodiments, one or more engines maybe arranged to identify one or more models that may be able to providean output that is appropriate for the agent characteristics informationor metrics associated with the agent. Accordingly, in some embodiments,the protocol used to evaluate the agent characteristics information ormetrics associated with the agent may be different depending on thestructure of the selected model. In some embodiments, each model may beassociated with meta-data that identifies the type of structure of themodel. In other embodiments, a model or its corresponding parametermodel may define one or more protocols that it may be compatible with.

In one or more of the various embodiments, one or more engines maytraverse decision model 500 to select or execute one or more models,portions of one or more models, or sub-models in one or more models. Insome of the various embodiments, traversing decision model 500 may beginat root node 502, continue through one or more edges 504, optionallycontinue through one or more intermediate nodes 506, and conclude at oneor more leaf nodes 508. In some embodiments, the path followed indecision model 500 may depend on conditions, outputs of model portions,or outputs of sub-models at each root node 502 or intermediate node 506.In some embodiments, each edge 504 may represent a portion of a paththat is defined by the output of the immediately preceding root orintermediate node 502, 506.

In one or more of the various embodiments, each root or intermediatenode 502, 506 may represent a condition or requirement associated withone or more models, such as the example models in the example repositoryof FIG. 6, and each leaf node 508 may represent an identifier, such asone of the model identifiers or family identifiers of FIG. 6. Forexample, the initial edge followed after root node 502 may depend on thetype of agent, the next edge followed after the intermediate node towhich the initial edge leads may depend on whether a particular materialmakeup of the agent is above or below a threshold identified by theintermediate node, and the next edge may lead to a leaf node thatidentifies a model or family identifier associated with a model, modelportion, or sub-model to execute. In some of the various embodiments,each root or intermediate node 502, 506 may represent a decision to bemade within a model, model portion, or sub-model that is being executed.For example, the initial edge followed after root node 502 may depend ona resource intake type being analyzed when executing the exampleresource model of FIG. 7, the next edge followed after the intermediatenode to which the initial edge leads may depend on which familyidentifier has a highest count among the resources that have resourcecharacteristics that match the resource intake type, and the next edgemay lead to a leaf node that identifies a resource or resource family toemploy in the next resource intake of the resource intake type.Accordingly, in some embodiments, each leaf node 508 may represent aclass label, which may identify an action to be taken (for example,selecting a model, model portion, or sub-model identified by the classlabel, selecting a resource, resource family, or others to be employed,or others). In some embodiments, one or more engines may execute one ormore models, model portions, or sub-models during the traverse ofdecision model 500 based on the path followed and may employ one or moreoutputs of the one or more executed models, model portions, orsub-models in the evaluation at subsequent node.

FIG. 6 shows a logical representation of example performance modelrepository 600 that may be employed by system 400. In one or more of thevarious embodiments, performance model repository 600 may include one ormore data objects (for example, records or others) that may representperformance models, such as performance models 424. In some of thevarious embodiments, performance model repository 600 may include anumber of named attributes, such as ID 602, Family_ID 604, Model 606,First_Condition 608 through A^(th)_Condition 610,First_Input_Requirement 612 through B^(th)_Input_Requirement 614,First_Output_Requirement 616, C^(th)_Output_Requirement 618, or others.In some embodiments, for each data object, the values for identifiers,such as those shown as entries for attribute 602 or others may besequential numbers, non-sequential numbers, strings, or others. In theexample shown in FIG. 6, each data object may be defined orcharacterized by one or more values associated with the namedattributes. For example, data object 620 with ID of one has a Family_IDof 0, Model of Model K, First_Condition of Condition T, A^(th)_Conditionof Condition AC, First_Input_Requirement of Requirement AL,B^(th)_Input_Requirement of Requirement AU, First_Output_Requirement ofRequirement BD, and C^(th)_Output_Requirement of Requirement BM.

In one or more of the various embodiments, if model repository 600involves hierarchies (for example, trees or others for one or moremodels, sets of models, portions of one or more models, sub-models ofone or more models, or others), nested data models or objects, or otherrelationships, Family_ID values associated with attribute 604 mayreference ID values associated with attribute 602 or others.Accordingly, in some of the various embodiments, model repository 600may be self-referential, thereby facilitating querying and providinginformation associated with relationships between multiple models, modelportions, or sub-models without referencing a further model repositorythat includes data objects that represent relationships. Examples ofrelationships may include being associated with the same or relatedagent characteristics, portions of agent characteristics, sets of agentcharacteristics, purposes, activities, geographic or logicalterritories, supervisory entities, or others.

In the example shown in FIG. 6, data object 620 represents a performancemodel (Model K) that is associated with no parent models, one or moreagent characteristics or conditions (or ranges of agent characteristicsor conditions) that may be employed to select the performance model (forexample, Condition T, Condition AC, or others), one or more requirements(or requirement ranges) that one or more values of one or more inputparameters must satisfy to be validly analyzed by the performance model(for example, Requirement AL, Requirement AU, or others), one or morerequirements (or requirement ranges) that one or more output values ofone or more output parameters must satisfy to be considered one or morevalid outputs (for example, Requirement BD, Requirement BM, or others),or others. In contrast, in the example shown in FIG. 6, data object 622with ID of G has a Family_ID of F, Model of Model P, First_Condition ofCondition Y, A^(th)_Condition of Condition AH, First_Input_Requirementof Requirement AQ, B^(th)_Input_Requirement of Requirement AZ,First_Output_Requirement of Requirement BI, andC^(th)_Output_Requirement of Requirement BR. Accordingly, in one or moreof the various embodiments, data object 622 may represent a performancemodel (Model P) that is in the same family as performance models with IDof F and E (F being a parent of G and a child of E) and that isassociated with one or more agent characteristics or conditions (orranges of agent characteristics or conditions) that may be employed toselect the performance model (for example, Condition Y, Condition AH, orothers), one or more requirements (or requirement ranges) that one ormore values of one or more input parameters must satisfy to be validlyanalyzed by the performance model (for example, Requirement AQ,Requirement AZ, or others), one or more requirements (or requirementranges) that one or more output values of one or more output parametersmust satisfy to be considered one or more valid outputs (for example,Requirement BI, Requirement BR, or others), or others.

In one or more of the various embodiments, multiple data objects inmodel repository 600 may form one or more portions or sub-models of amodel, as defined by one or more of the attributes, such as Fam_ID 604.In some of the various embodiments, a model may be traversed byexecuting one or more processes or actions defined by one or more dataobjects associated with one or more portions or sub-models in the model(for example: one or more actions or processes defined by or associatedwith one or more outputs or others of one or more mathematical modelssuch as equations, prediction models as described with regard toprediction engine 416, decision models such as decision model 500, orothers included in or referenced by Model attribute 606; executing oneor more models, model portions, or sub-models included in Modelattribute 606; or others). In some embodiments, one or more modelportions or sub-models may be selected for execution when traversing themodel based on one or more characteristics or conditions (or ranges ofcharacteristics or conditions) associated with one or more elementsbeing evaluated or analyzed (for example, one or more amounts, portionsof agent characteristics information, or others). Accordingly, in someembodiments, a hierarchy in a model may be represented by one or morevalues in Fam_ID attribute 604. In some embodiments, one or more modelportions or sub-models may be associated with multiple paths within oneor more models. Accordingly, in some embodiments, performance modelrepository 600 may facilitate dynamically selecting one or more models,model portions, or sub-models based on one or more characteristics orconditions of one or more elements or phases in one or more processes,actions, control sessions, or others, thereby facilitating improvingcomputational performance of system 400, reliability of system 400,consistency throughout system 400 when updates are provided, or others.

In one or more of the various embodiments, system 400 may include one ormore repositories that include one or more data objects for each elementin or associated with system 400. In some of the various embodiments,each element type (for example, agents, control sessions, interactions,actions, metrics, or others) may have a dedicated repository thatincludes data objects for each element of the element type. In someembodiments, each data object for each element may have attributes thatcorrespond to features or characteristics of the element type of theelement. For clarity, data repository 600 is shown using tabular format.In some embodiments, data sets or data objects may be arrangeddifferently, such as using different formats, data structures, objects,or others. For example, data repository 600 may be structured as a JSONobject (for example, a JSON tree or others).

FIG. 7 illustrates a logical representation of example resource model700 that may be employed by system 400. In one or more of the variousembodiments, resource model 700 may include one or more data objects(for example, records or others) that may represent resources availableto one or more agents or resources that resource consumption controlcomputer 406 may instruct one or more agents to consume. In some of thevarious embodiments, resource model 700 may include a number of namedattributes, such as ID 702, Fam_ID 704, Normalized Units 706,Specific_Units 708, Resource 710, First_Characteristic 712 throughA^(th)_Characteristic 714, or others. In some embodiments, for each dataobject, the values for identifiers, such as those shown as entries forattribute 702 or others may be sequential numbers, non-sequentialnumbers, strings, or others. In the example shown in FIG. 7, each dataobject may be defined or characterized by one or more values associatedwith the named attributes. For example, data object 716 with ID of onehas a Family_ID of 0, Normalized Units of Unit I, Specific_Units of UnitR, Resource of Resource AA, First_Characteristic of Characteristic AJ,and A^(th)_Characteristic of Characteristic AS.

In one or more of the various embodiments, if resource model 700involves hierarchies (for example, resource types, resource families,intake types, objective types, or others), nested data objects, or otherrelationships, Family_ID values associated with attribute 704 mayreference ID values associated with attribute 702 or others.Accordingly, in some of the various embodiments, resource model 700 maybe self-referential, thereby facilitating querying and providinginformation associated with relationships between multiple resourceswithout referencing a further relationship model that includes dataobjects that represent relationships. Examples of relationships mayinclude being associated with the same or related resource type,resource family, intake type, objective type, activity type, purposes,activities, geographic or logical territories, supervisory entities, orothers).

In the example shown in FIG. 7, data object 716 represents a resource(Resource AA) that is associated with no parent resources, an amount ofnormalized units that equates to an amount of specific units of theresource, one or more characteristics (for example, resource type,resource family, intake type such as breakfast, objective type, activitytype, purposes, activities, geographic or logical territories in whichthe resource is readily available, geographical or logical territoriesin which the resource is has one or more positive or negativeenjoyabilities such as taste ratings, supervisory entities, enjoyabilitysuch as a taste rating, pairings or pairing types that are acceptable orunacceptable, likelihood of allergies, warnings, or others), or others.In contrast, in the example shown in FIG. 7, data object 718 with ID ofE has a Family_ID of D, Normalized Units of Unit N, Specific_Units ofUnit W, Resource of Resource AF, First_Characteristic of CharacteristicAO, and A^(th)_Characteristic of Characteristic AX. Accordingly, in oneor more of the various embodiments, data object 718 may represent aresource (Resource AF) that is in the same family as resources with IDof D and C (D being a parent of E and a child of C) and that isassociated with an amount of normalized units that equates to an amountof specific units of the resource, one or more characteristics, orothers. In some embodiments, one or more resource classification engines408 may generate or populate one or more resource models 700 or dataobjects in one or more resource models 700. In other embodiments, one ormore resource models 700 or data objects in one or more resource models700 may be generated or populated based on input into a user interface.

In one or more of the various embodiments, one or more resource recipeengines 410 may employ one or more resource models 700 or data objectsin one or more resource models 700 to generate one or more resourcerecipes to employ in one or more control sessions. In some of thevarious embodiments, one or more resource recipes may be stored ordefined by one or more recipe models that have similar structures orcharacteristics to resource model 700, with attributes that correspondto the resource recipes. In some embodiments, one or more resourceconsumption control engines 412 may employ one or more resource orrecipe models or one or more data objects in one or more resource orrecipe models to generate one or more portions of the resourceconsumption control instructions. For clarity, resource model 700 isshown using tabular format. In some embodiments, data sets or dataobjects may be arranged differently, such as using different formats,data structures, objects, or others. For example, resource model 700 maybe structured as a JSON object (for example, a JSON tree or others).

FIG. 8 shows an overview flowchart of example process 800 forcontrolling resource consumption. One or more portions of process 600may be performed by one or more engines in one or more client computersor network computers (for example: one or more performance trackingengines 218, performance monitor engines 222, or others in one or moreclient computers 200; one or more resource classification engines 318,resource recipe generation engines 322, consumption control engines 324,metrics analysis engines 326, or others in one or more network computers300; or others), such as one or more client or network computersassociated with or included in one or more agents 402, performancemonitors 404, resource consumption control computer 406, or others. Inone or more of the various embodiments, after a start block, at block802, one or more control sessions may be initialized for one or moreagents, such as a control session to control resource consumption by anagent. In some of the various embodiments, initializing the controlsession may include assigning one or more performance monitors to theagent or the control session, onboarding or otherwise initializingcommunication with the agent, obtaining information (for example,identification information, contact information, agent characteristicsinformation, or others) from the agent, or others. In some embodiments,initializing the control session may begin with the agent, an entitythat supervises the agent, the performance monitor, or otherscommunicating one or more requests over one or more networks to aresource consumption control computer. In some embodiments, the requestmay be provided via a user interface (for example, a web page,application, or others) in various forms, such as email, user interface(UI) notification, instant message, or others.

At block 804, in one or more of the various embodiments, the one or morecontrol sessions may be launched. In some of the various embodiments,launching the one or more control sessions may include providing one ormore resource consumption instructions to the one or more agentsassociated with the one or more control sessions.

At block 806, in one or more of the various embodiments, one or moremetrics associated with the one or more launched control sessions may beobtained. In some of the various embodiments, the one or more metricsmay be based on one or more actions of the one or more agents associatedwith the one or more control sessions (for example, one or more actionsperformed responsive to the one or more resource consumptioninstructions, resource consumption information, objective performanceinformation, mouse clicks, types of clicks, mouse hover time, opens,visits, refreshes, timing of actions, most-recent login time, quantitiesof actions, or others).

At decision block 808, in one or more of the various embodiments, if theone or more control sessions have ended, control may return to a callingprocess; otherwise, control may flow to block 810. In some of thevarious embodiments, a control session may end based on the occurrenceof one or more conditions, such as meeting or exceeding one or moreobjectives associated with the control sessions, expiration of a definedduration of the control session, arbitrary feedback from one or moreagents, supervisory entities, performance monitors, or others,exhausting one or more resources, an agent or supervisory entity ceasingto participate in the control session, or others.

At block 810, in one or more of the various embodiments, optionally, theone or more control sessions may be modified based on the obtainedmetrics. Examples of modifying a control session may include calibratingone or more components in system 400, such as one or more of thefollowing: modifying one or more parameters, variables, coefficients,constants, or other components of one or more models, portions of one ormore models, sub-models of one or more models, or others; modifying oneor more outputs of one or more models, portions of one or more models,sub-models of one or more models, or others; or others. In some of thevarious embodiments, one or more outputs of one or more models, portionsof one or more models, sub-models of one or more models may be modified.Block 810 is optional because the one or more control sessions maycontinue without modification. For example, analysis of the one or moremetrics may indicate that one or more candidate modifications will notor is unlikely to increase the effectiveness of one or more controlsessions. From block 810, control flows to block 806 to continue the oneor more control sessions.

In some embodiments, process 800 may continue operating until one ormore events occur, such as meeting or exceeding one or more objectivesassociated with the control sessions, expiration of a defined durationof the control session, arbitrary feedback from one or more agents,supervisory entities, performance monitors, or others, exhausting one ormore resources, an agent or supervisory entity ceasing to participate inthe control session, a user configures process 800 to terminateoperation, or others. Next, control may be returned to a callingprocess.

FIG. 9 illustrates a logical flow diagram of example process 900 forinitializing or launching a control session. One or more portions ofprocess 900 may be performed by one or more engines in one or moreclient computers or network computers (for example: one or moreperformance tracking engines 218, performance monitor engines 222, orothers in one or more client computers 200; one or more resourceclassification engines 318, resource recipe generation engines 322,consumption control engines 324, metrics analysis engines 326, or othersin one or more network computers 300; or others), such as one or moreclient or network computers associated with or included in one or moreagents 402, performance monitors 404, resource consumption controlcomputer 406, or others. In one or more of the various embodiments, oneor more portions of process 900 may correspond to or be included in oneor more of blocks 802, 804, or others. In some of the variousembodiments, after a start block, at block 902, agent characteristicsinformation associated with an agent may be obtained to initialize orlaunch a control session, such as a control session to control resourceconsumption by an agent. In some embodiments, the agent, an entity thatsupervises the agent, a performance monitor assigned to the agent, orothers may communicate one or more portions of the agent characteristicsinformation over one or more networks to one or more resourceconsumption control computers 406. In some embodiments, one or moreclient computers 200 may monitor or provide one or more portions of theagent characteristics information based on one or more user interactionswith a user interface (for example, a web page, application, or others)in various forms, such as email, user interface (UI) notification,instant message, or others. In other embodiments, one or more portionsof the agent characteristics information may be provided or generated byone or more sensors or sensor interfaces (for example, one or morecameras 240, video interfaces 242, sensor interfaces 262, or others)associated with the agent (for example, one or more sensors or sensorinterfaces that are included in client computer 200, that are incommunication with client computer 200, or others).

At block 904, in one or more of the various embodiments, one or moreagent data objects associated with the agent may be generated based onone or more portions of the agent characterization information. In someof the various embodiments, the one or more agent data objects may begenerated or populated employing one or more processes as described withregard to system 400, one or more data structures with regard to FIG. 6or 7, or others. For example, a record may be appended to a tabular datastructure and populated with one or more portions of the agentcharacteristics information being included in various fields in therecord that are associated with various attributes that correspond tothe one or more portions of the agent characteristics information.

At block 906, in one or more of the various embodiments, one or moreconditions of one or more candidate performance models, candidate modelportions, or candidate sub-models (for example, one or more candidatemodels 424 in one or more performance model repositories 422, such asrepository 600) may be evaluated based on one or more portions of theone or more agent data objects that include or represent the one or moreportions of agent characteristics information. In some embodiments, oneor more values of one or more portions of the agent characteristicsinformation may be compared to one or more conditions or ranges ofconditions associated with the one or more candidate performance models,candidate model portions, or candidate sub-models (for example, one ormore values included in one or more attributes, such as attributes 608,610, or others). In some embodiments, a candidate model, model portion,or sub-model may be considered a match if the one or more values of oneor more portions of the agent characteristics information equals, fallswithin a predetermined range of, or falls between one or more values ofone or more conditions or ranges of conditions associated with thecandidate performance model, model portion, or sub-model. In otherembodiments, a candidate model, model portion, or sub-model may beconsidered a match if the one or more values of one or more portions ofthe agent characteristics information are closer to one or more valuesof one or more conditions or ranges of conditions associated with thecandidate performance model, model portion, or sub-model than one ormore values of one or more conditions or ranges of conditions associatedwith one or more other candidate performance models, model portions, orsub-models. In some embodiments, a candidate model, model portion, orsub-model may be considered a match based on one or more portions of oneor more selection processes described with regard to system 400, such asone or more selection processes based on one or more machine learningmodels, linear regression models, heuristics models, or others derivedfrom relevant historical metrics for one or more other control sessions,one or more predictions discovered by prediction engine 416,deep-learning networks, or others.

At decision block 908, in one or more of the various embodiments, if oneor more of the candidate models, model portions, or sub-models are amatch based on the evaluation, control may flow to block 910; otherwise,control may flow to block 906 to evaluate one or more further candidatemodels, model portions, or sub-models. In some of the variousembodiments, if no match is found after evaluating a predeterminednumber of the candidate performance models, model portions, orsub-models, the criteria for determining a match may be adjusted. Forexample, based on the criteria adjustment, a candidate model, modelportion, or sub-model may be considered a match if the one or morevalues of one or more portions of the agent characteristics informationare closer to one or more values of one or more conditions or ranges ofconditions associated with the candidate performance model, modelportion, or sub-model than one or more values of one or more conditionsor ranges of conditions associated with one or more other candidateperformance models, model portions, or sub-models.

At block 910, in one or more of the various embodiments, the one or morematched performance models, model portions, or sub-models may betraversed to generate a predicted energy consumption needs amount basedon one or more portions of the agent characteristics information. Insome of the various embodiments, traversing a matched performance modelmay include executing one or more model portions or sub-models in thematched performance model based on the one or more portions of the agentcharacteristics information to provide one or more outputs and executingone or more other model portions or sub-models in the matchedperformance model based on the one or more outputs.

At block 912, in one or more of the various embodiments, the predictedenergy consumption needs amount may be transformed into resourcedistribution information. In some of the various embodiments, theresource distribution information may indicate one or more amounts ofone or more resources or resource types that the agent should intakeduring one or more expected resource intake sessions based on one ormore portions of the agent characteristics information.

At block 914, in one or more of the various embodiments, one or moreresource consumption instructions may be provided to the agent based onone or more portions of the resource distribution information. In someof the various embodiments, resource consumption instructions mayinstruct the agent to intake or consume one or more amounts of one ormore resources or resource types per interval, intake session, orothers. In some embodiments, different types or formats of resourceconsumption instructions may be provided based on agent type, agentdisplay type, or others.

In some embodiments, process 900 may continue operating until thecontrol session terminates, the control session has fully launched, or auser configures process 900 to terminate operation. Next, control may bereturned to a calling process.

FIG. 10 shows a logical flowchart of example process 1000 forinitializing or launching a control session. One or more portions ofprocess 1000 may be performed by one or more engines in one or moreclient computers or network computers (for example: one or moreperformance tracking engines 218, performance monitor engines 222, orothers in one or more client computers 200; one or more resourceclassification engines 318, resource recipe generation engines 322,consumption control engines 324, metrics analysis engines 326, or othersin one or more network computers 300; or others), such as one or moreclient or network computers associated with or included in one or moreagents 402, performance monitors 404, resource consumption controlcomputer 406, or others. In one or more of the various embodiments, oneor more portions of process 1000 may correspond to or be included in oneor more of blocks 802, 804, 902, 904, 910, 912, 914, or others. In someof the various embodiments, after a start block, at block 1002, agentcharacteristics information associated with an agent may be obtained toinitialize or launch a control session, such as a control session tocontrol resource consumption by an agent. In some embodiments, theagent, an entity that supervises the agent, a performance monitorassigned to the agent, or others may communicate one or more portions ofthe agent characteristics information over one or more networks to oneor more resource consumption control computers 406. In some embodiments,one or more portions of the agent characteristics information may beprovided via a user interface (for example, a web page, application, orothers) in various forms, such as email, user interface (UI)notification, instant message, or others. In other embodiments, one ormore portions of the agent characteristics information may be providedor generated by one or more sensors or sensor interfaces (for example,one or more cameras 240, video interfaces 242, sensor interfaces 262, orothers) associated with the agent (for example, one or more sensors orsensor interfaces that are included in client computer 200, that are incommunication with client computer 200, or others).

At block 1004, in one or more of the various embodiments, a predictedbaseline energy expenditure rate of the agent may be generated based onone or more portions of the agent characteristics information. In someof the various embodiments, one or more of the selected or matchedperformance models, model portions, or sub-models described with regardto one or more of blocks 906, 908, or others may be employed, executed,or traversed to generate the predicted baseline energy expenditure rate.

In one or more of the various embodiments, one or more equations may beselected based on one or more portions of the agent characteristicsinformation, such as agent type (for example, internal combustionengine, Stirling engine, male gender, female gender, or others), weight,known material makeup (for example, quantities or percentages of one ormore elements, such as aluminum, iron, body fat, or others), age, size(for example, displacement, number of cylinders, height, or others),estimated material makeup, activity rating (for example, a valueselected on a predetermined scale such as a value between zero and onethat indicates an amount of use above rest per predetermined period,such as a duration or intensity of the use, or others), or others. Forexample, the one or more equations may be selected based on whether theage is greater than or less than one or more predetermined thresholds(for example, 19 years old or others), gender, whether body fat is knownor estimated, whether body fat is greater than or less than one or morepredetermined thresholds (for example, 20% for males, 25% for females,or others), whether the activity rating is greater than or less than oneor more predetermined thresholds (for example, 50%, 1.5 on a scale from1-2, or others), or others. In some of the various embodiments, one ormore outputs of the one or more selected equations may be weighted basedon the one or more portions of the agent characteristics information. Insome embodiments, the one or more weighted outputs may be summed toprovide the predicted baseline energy expenditure rate of the agent. Forexample, in a control session for a male agent with an age that isgreater or equal to 19, an estimated body fat of greater than or equalto 20%, and an activity rating of less than 1.5, a Mifflin-St. Jeorequation (for example, [9.99× weight in kilograms]+[6.25× height inmeters×100]−[4.92× age in years]+5, or others) and a Katch-McArdleequation (for example, [21.6× [[1−body fat percentage as a decimalvalue]× weight in kilograms]]+370, or others) may be selected, with theoutput of the Mifflin-St. Jeor equation being multiplied by 0.8 and theoutput of the Katch-McArdle equation being multiplied by 0.2, and theweighted outputs of the equations may be summed to provide a BMR amountfor the agent.

At block 1006, in one or more of the various embodiments, a predictedinterval energy expenditure amount may be generated based on one or moreportions of the agent characteristics information and the predictedbaseline energy expenditure rate. In some of the various embodiments,one or more of the selected or matched performance models, modelportions, or sub-models described with regard to one or more of blocks906, 908, or others may be employed, executed, or traversed to generatethe predicted interval energy expenditure amount.

In one or more of the various embodiments, one or more equations may beselected based on one or more portions of the agent characteristicsinformation, such as activity rating (for example, a value selected on apredetermined scale such as a value between zero and one that indicatesan amount of use above rest per predetermined period, such as a durationor intensity of the use, or others), lifestyle rating (for example, avalue selected on a predetermined scale such as between 0 and 1 thatindicates how physically active the agent is during the agent's timeoutside of the activities measured by the activity rating, such as theagent's professional time and non-training leisure time or others),impairment status (for example, one or more values that indicate whetherthe agent is damaged or injured, the seriousness of the damage orinjury, or others), or others. For example, the one or more equationsmay be selected based on whether the age is greater than or less thanone or more predetermined thresholds (for example, 19 years old orothers), gender, or others), or others. In some of the variousembodiments, the predicted baseline energy expenditure rate and one ormore values of one or more portions of the agent characteristicsinformation may be employed as inputs to the one or more selectedequations, and one or more outputs of the selected equations may providethe predicted interval energy expenditure amount. For example, in acontrol session for an agent with an age that is greater or equal to 19,a sum of the number one and the impairment status (for example, aselected value of 0 for no impairment, 0.2 for minor impairment, 0.4 formajor impairment, or others) may be added to a sum of the activityrating (for example, a selected value between 0 and 1 that indicates anamount of use above rest per day or others, such as during training) andthe lifestyle rating (for example, a selected value between 0 and 1 thatindicates how physically active the agent is during the agent's timeoutside of the activities measured by the activity rating, such as theagent's professional time and non-training leisure time) to provide aresult, and the result may be multiplied by the predicted baselineenergy expenditure rate to provide the predicted interval energyexpenditure amount.

In one or more of the various embodiments, the activity rating employedto generate the predicted interval energy expenditure amount mayrepresent an estimated activity rating for the agent. In some of thevarious embodiments, the activity rating may represent an averageactivity rating for the agent. In some embodiments, the activity ratingmay represent actual tracked activity intensity or duration for aparticular interval based on metrics obtained from the agent (forexample, metrics input in a user interface, metrics provided based onsensor data, or others). In some embodiments, historical actual trackedactivity ratings for particular intervals may be employed by one or moreprediction engines (for example, prediction engine 416 or others) topredict activity ratings for particular intervals during the controlsession. For example, the historical actual tracked activity ratings mayindicate that the agent typically has an activity rating of 1.8 onMondays, 1.6 on Tuesdays, 1.7 on Wednesdays, and 1.4 on Thursdays, witha predicted activity rating being unavailable for Fridays-Sundays.Accordingly, in some embodiments, the predicted interval energyexpenditure amount may represent an amount of energy that the agent isexpected to expend on average per interval based on the estimatedactivity rating or the average activity rating for the agent. In someembodiments, the predicted average-interval energy expenditure amountmay be employed as a default value for the predicted interval energyexpenditure amount. In some embodiments, the predicted interval energyexpenditure amount may represent an amount of energy that the agent isexpected to expend in a particular interval based on the trackedactivity rating or the predicted activity rating for the particularinterval. In some embodiments, the predicted particular-interval energyexpenditure amount may be employed as the value for the predictedinterval energy expenditure amount when available or when one or moreconditions are satisfied, such as the predicted particular-intervalenergy expenditure amount exceeding the predicted average-intervalenergy expenditure amount or others. In some embodiments, the predictedparticular-interval energy expenditure amount may be generated based onthe same one or more models, model portions, or sub-models as thepredicted average-interval energy expenditure amount. In otherembodiments, the predicted particular-interval energy expenditure amountmay be generated based on a different one or more models, modelportions, or sub-models that may be selected based on the availabilityof the tracked activity rating or the predicted activity rating for theparticular interval or one or more other conditions.

At block 1008, in one or more of the various embodiments, a predictedenergy consumption needs amount may be generated based on one or moreportions of the agent characteristics information and the predictedinterval energy expenditure amount. In some of the various embodiments,one or more of the selected or matched performance models, modelportions, or sub-models described with regard to one or more of blocks906, 908, or others may be employed, executed, or traversed to generatethe predicted energy consumption needs amount.

In one or more of the various embodiments, one or more equations may beselected based on one or more portions of the agent characteristicsinformation, such as one or more agent objectives or others. Forexample, in a control session for an agent with an objective of changingweight, an equation may be selected to multiply the number 1,100 by adesired weight change per week in kilograms to provide a product, andthe product may be summed with the predicted interval energy expenditureamount to provide a temporary predicted energy consumption needs amount.In some of the various embodiments, the one or more equations may beselected based on a comparison of the one or more agent objectives toone or more thresholds. For example, in a control session where theagent's temporary predicted energy consumption needs amount is less thanthe predicted baseline energy expenditure rate, the predicted baselineenergy expenditure rate may be provided as the predicted energyconsumption needs amount. As another example, if the activity rating isless than a predetermined threshold (for example, 0.2 or others) and thetemporary predicted energy consumption needs amount minus the predictedinterval energy expenditure amount is greater than a predetermined value(for example, 500 or others), the sum of the predicted interval energyexpenditure amount and the predetermined value may be provided as thepredicted energy consumption needs amount.

At block 1010, in one or more of the various embodiments, the predictedenergy consumption needs amount may be transformed into amounts ofspecific-units needs for multiple resource types based on one or moreportions of the agent characteristics information. In some of thevarious embodiments, one or more of the selected or matched performancemodels, model portions, or sub-models described with regard to one ormore of blocks 906, 908, or others may be employed, executed, ortraversed to transform the predicted energy consumption needs amountinto the specific-units needs amounts for the multiple resource types.

In one or more of the various embodiments, one or more equations may beselected based on one or more portions of the agent characteristicsinformation, such as known material makeup (for example, quantities orpercentages of one or more elements, such as aluminum, iron, body fat,or others), age, size (for example, displacement, number of cylinders,height, or others), estimated material makeup, activity rating (forexample, a value selected on a predetermined scale such as a valuebetween 0 and 1 that indicates an amount of use above rest perpredetermined period, such as a duration or intensity of the use, orothers), impairment status, or others. For example, the one or moreequations may be selected based on whether the age is greater than orless than one or more predetermined thresholds (for example, 19 yearsold or others), gender, whether body fat is known or unknown, whetherbody fat is greater than or less than one or more predeterminedthresholds (for example, 25% or others), whether the activity rating isgreater than or less than a prior period or interval for the agent byone or more predetermined thresholds (for example, greater than theactivity rating of the prior period or interval by 0.2 or more orothers), or others. In some of the various embodiments, the output ofthe one or more selected equations may be provided as the specific-unitsneeds for the multiple resource types. For example, in a control sessionof an agent with a known or estimated body fat percentage that isgreater than or equal to a predetermined threshold (for example, 25% orothers), the agent objective (for example, desired weight change perweek in kilograms) may be squared and subsequently multiplied by acoefficient (for example, 0.1809 or others) to produce a first product,the agent objective (for example, desired weight change per week inkilograms) may be squared and subsequently multiplied by a coefficient(for example, 0.3558 or others) to provide a second product, the secondproduct may be subtracted from the first product to provide adifference, the difference may be summed with another value (forexample, 1.9849 or others), and the sum may be multiplied by the agentweight in kilograms to provide an amount of specific-units needs forprotein (for example, as described in grams or others). As anotherexample, in a control session of an agent, the known or estimated bodyfat of the agent may be subtracted from the number one to provide adifference, the difference may be multiplied by a coefficient (forexample, 0.5 or others), and the difference may be multiplied by theweight of the agent in kilograms to provide an amount of specific-unitsneeds for fat (for example, as described in grams or others). As afurther example, in a control session of an agent, the activity ratingof the agent (for example, a value selected on a predetermined scalesuch as a value between 0 and 10 that indicates an amount of use aboverest per predetermined period, such as a duration or intensity of theuse, or others) may be multiplied by a coefficient (for example, 0.1 orothers) to provide a first product, the first product may be summed withthe number one to provide a first sum, the first sum may be squared toprovide a second product, the second product may be multiplied by acoefficient (for example, 0.19191 or others) to provide a third product,the first product may be multiplied by a coefficient (for example,0.86831 or others) to provide a fourth product, another value (forexample, 0.31988 or others) may be subtracted from the fourth product toprovide a first difference, the third product may be subtracted from thefirst difference to provide a second difference, the second differencemay be multiplied by a coefficient (for example, 0.25 or others) toprovide a fifth product, the fifth product may be multiplied by thepredicted energy consumption needs amount to provide a sixth product,and the sixth product may be divided by the weight of the agent inkilograms to provide an amount of specific-units needs for carbohydrates(for example, as described in grams or others).

In one or more of the various embodiments, the activity rating employedto generate one or more specific-units needs amounts for one or moreresource types may represent the estimated activity rating, the averageactivity rating, the actual tracked activity intensity or duration for aparticular interval, or the predicted activity rating for a particularinterval. Accordingly, in some of the various embodiments, the amount ofspecific-units needs for each of one or more resource types mayrepresent an amount of specific-units for the resource type that theagent is expected to need on average per interval to obtain the agentobjectives based on the estimated activity rating or the averageactivity rating for the agent. In some embodiments, the average-intervalspecific-units needs amount for each of one or more resource types maybe employed as a default value for the specific-units needs amount forthe resource type. In some embodiments, the amount of specific-unitsneeds for each of one or more resource types may represent an amount ofspecific-units for the resource type that the agent is expected to needin a particular interval based on the tracked activity rating or thepredicted activity rating for the particular interval. In someembodiments, the particular-interval specific-units needs amount foreach of one or more resource types may be employed as the value for thespecific-units needs amount for the resource type when available or whenone or more conditions are satisfied, such as the particular-intervalspecific-units needs amount for the resource type exceeding theaverage-interval specific-units needs amount for the resource type orothers.

In one or more of the various embodiments, the particular-intervalspecific-units needs amount for each of one or more resource types maybe generated based on the same one or more models, model portions, orsub-models as the average-interval specific-units needs amount for theresource type. In other embodiments, the particular-intervalspecific-units needs amount for each of one or more resource types maybe generated based on a different one or more models, model portions, orsub-models that may be selected based on the availability of the trackedactivity rating or the predicted activity rating for the particularinterval or one or more other conditions. In some of the variousembodiments, a temporary amount of specific-units needs for each of oneor more resource types may be generated based on activity time (forexample, seconds, minutes, or other units of time of use above rest),and the temporary amount may be modified based on activity intensity(for example, energy expended during the use above rest) to generate theparticular-interval specific-units needs amount for the resource type.For example, in a control session of an agent, the total activity time(for example, seconds, minutes, or other units of time of use above restas tracked, predicted, or others) for a particular interval may bemultiplied by a coefficient (for example, 0.06277 or others) to providea first product, the first product may be summed with a constant (forexample, 1.69034 or others) to provide a first sum, the square of thetotal activity time may be multiplied by a coefficient (for example,0.00009 or others) to provide a second product, the second product maybe subtracted from the first sum to provide a first difference, theactivity intensity (for example, calories or other units of energyexpended during the use above rest as tracked, predicted, or others) maybe divided by the weight of the agent in kilograms to provide a firstquotient, the first quotient may be divided by the total activity timeto provide a second quotient, the second quotient may be multiplied by acoefficient (for example, 7.78210 or others) to provide a third product,a constant (for example, 0.02724 or others) may be subtracted from thethird product to provide a second difference, and the second differencemay be multiplied by the first difference to provide an amount ofspecific-units needs for carbohydrates (for example, as described ingrams or others) for a particular interval. At block 1012, in one ormore of the various embodiments, the specific-units needs amount foreach of the multiple resource types may be transformed into an amount ofnormalized-units needs for the resource type based on thespecific-units-needs amount for the resource type and thenormalized-units needs amount for one or more other resource types. Insome of the various embodiments, one or more of the selected or matchedperformance models, model portions, or sub-models described with regardto one or more of blocks 906, 908, or others may be employed, executed,or traversed to transform the specific-units needs amounts for themultiple resource types into the normalized-units needs amounts for themultiple resource types. In some embodiments, transforming thespecific-units needs amounts for the multiple resource types intoamounts of normalized-units needs for the resource types may facilitatestandardizing normalized units according to specific units, mayfacilitate adjusting for errors in agent perception or measurements, orothers. In some embodiments, transforming the specific-units needsamounts for the multiple resource types into amounts of normalized-unitsneeds for the resource types may facilitate overcoming one or more ofthe deficiencies in employing the “Reference Amounts CustomarilyConsumed” (RACC) tables defined by the United States Food and DrugAdministration (FDA) to generate label serving size requirements. Forexample, label serving size requirements according to the FDA RACCtables lack consistency in definitions according to energy andmacronutrients that may lead to inaccuracies, and, in contrast, one ormore resource consumption control computers (for example, one or moreresource consumption control computers 406 or others) may provideconsistently defined normalized units based on specific units in termsthat agents can readily interpret, such as volumes or others.

For example, in a control session of an agent, an amount ofspecific-units needs for protein (for example, an amount described ingrams or others) may be multiplied by a coefficient (for example, 0.04or others) to provide a first product, the amount of specific-unitsneeds for protein may be multiplied by a coefficient (for example, 4 orothers) to provide a second product, the second product may besubtracted from the predicted energy consumption needs amount to providea difference, the difference may be multiplied by a coefficient (forexample, 0.02 or others) to provide a third product, the third productmay be multiplied by a coefficient (for example, 0.04 or others) toprovide a fourth product, and the fourth product may be subtracted fromthe first product to provide a normalized-units needs amount forprotein. As another example, in a control session of an agent, an amountof specific-units needs for carbohydrates (for example, an amountdescribed in grams or others) may be multiplied by a coefficient (forexample, 0.04 or others) to provide a normalized-units needs amount forcarbohydrates. As a further example, in a control session of an agent, anormalized-units needs amount for protein may be multiplied by acoefficient (for example, 4 or others) to provide a first product, anormalized-units needs amount for carbohydrates may be multiplied by acoefficient (for example, 2 or others) to provide a second product, thefirst and second products may be subtracted from a specific-units needsamount for fat (for example, an amount described in grams or others) toprovide a difference, and the difference may be multiplied by acoefficient (for example, 0.0909091 or others) to provide anormalized-units needs amount for fat.

At block 1014, in one or more of the various embodiments, thenormalized-units needs amounts for the multiple resource types may betransformed into resource distribution information based on one or moreportions of the agent characteristics information. In some of thevarious embodiments, one or more of the selected or matched performancemodels, model portions, or sub-models described with regard to one ormore of blocks 906, 908, or others may be employed, executed, ortraversed to transform the normalized-units needs amounts for themultiple resource types into the resource distribution information.

At block 1016, in one or more of the various embodiments, one or moreresource consumption control instructions may be provided to the agentbased on one or more portions of the resource distribution information.In some of the various embodiments, resource consumption instructionsmay instruct the agent to intake or consume one or more amounts of oneor more resources or resource types per interval, intake session, orothers.

In some embodiments, process 1000 may continue operating until thecontrol session terminates, the control session has fully launched, or auser configures process 1000 to terminate operation. Next, control maybe returned to a calling process.

FIG. 11 illustrates a logical flow diagram of example process 1100 formodifying a control session. One or more portions of process 1100 may beperformed by one or more engines in one or more client computers ornetwork computers (for example: one or more performance tracking engines218, performance monitor engines 222, or others in one or more clientcomputers 200; one or more resource classification engines 318, resourcerecipe generation engines 322, consumption control engines 324, metricsanalysis engines 326, or others in one or more network computers 300; orothers), such as one or more client or network computers associated withor included in one or more agents 402, performance monitors 404,resource consumption control computer 406, or others. In one or more ofthe various embodiments, one or more portions of process 1100 maycorrespond to or be included in one or more of blocks 804, 806, 808,810, 902, 904, 910, 912, 914, 1002, 1008, 1010, 1012, 1014, 1016, orothers. In some of the various embodiments, after a start block, atblock 1102, one or more resource consumption control instructions may beprovided to the agent based on one or more portions of the resourcedistribution information. In some of the various embodiments, resourceconsumption instructions may instruct the agent to intake or consume oneor more amounts of one or more resources or resource types per interval,intake session, or others.

At block 1104, in one or more of the various embodiments, one or moremetrics associated with the control session may be obtained. In some ofthe various embodiments, the one or more metrics may be based on one ormore actions of the agent (for example, one or more actions performedresponsive to the one or more resource consumption instructions,resource consumption information, objective performance information,mouse clicks, types of clicks, mouse hover time, opens, visits,refreshes, timing of actions, most-recent login time, quantities ofactions, or others). In some embodiments, the one or more metrics may beobtained as the one or more metrics are accumulated or generated, at apredetermined phase in each interval, at a predetermined phase in eachperiod, when the agent connects to a network, or others. In someembodiments, the one or more metrics may indicate the amounts of one ormore resource types consumed by the agent in one or more intakesessions, intervals, or others, may indicate feedback associated withone or more activities (for example, one or more weights, performanceratings, material makeups, activity or training intensity, activity ortraining duration, or others), or others. In some embodiments, one ormore portions of the one or more metrics may be provided via a userinterface (for example, a web page, application, or others) in variousforms, such as email, user interface (UI) notification, instant message,or others. In other embodiments, one or more portions of the one or moremetrics may be provided or generated by one or more sensors or sensorinterfaces (for example, one or more cameras 240, video interfaces 242,sensor interfaces 262, or others) associated with the agent (forexample, one or more sensors or sensor interfaces that are included inclient computer 200, that are in communication with client computer 200,or others).

At decision block 1106, in one or more of the various embodiments, ifmetrics have been obtained for a predetermined number of qualifyingperiods (for example, two qualifying periods or others) have beenobtained, control may flow to block 1108; otherwise, control may flow toblock 1104. In some of the various embodiments, a qualifying period maybe defined as a set of a predetermined number of back-to-back intervals(for example, 4-7 back-to-back days or others) in which the number ofcomplete intervals meets or exceeds a minimum threshold (for example,four or more complete intervals or others). In some embodiments, acomplete interval may be defined as an interval for which resourceconsumption information has been obtained for each intake during theinterval (for example, discretely, in total, or others) and for whichobjective performance information has been obtained for the interval. Insome embodiments, an interval may be indicated as complete based on oneor more user inputs confirming that the interval is complete. In someembodiments, a qualifying period may include one or more incompleteintervals (for example, an interval for which resource consumptioninformation has not been obtained for one or more intakes during theinterval or for which objective performance information has not beenobtained for the interval) between two complete intervals in thequalifying period.

At block 1108, in one or more of the various embodiments, one or morepredicted changes to one or more portions of the agent characteristicinformation may be generated based on one or more obtained metrics andthe predicted interval energy expenditure amount. In some of the variousembodiments, one or more models, model portions, or sub-models may beselected by employing a similar process as described with regard to oneor more of blocks 906, 908, or others, and the one or more selectedmodels, model portions, or sub-models may be employed, executed, ortraversed to generate the one or more predicted changes to the one ormore portions of the agent characteristics information. In someembodiments, generating the one or more predicted changes may includegenerating the average expected objective performance per interval, asdescribed with regard to one or more processes of system 400.

At block 1110, in one or more of the various embodiments, one or moreobtained metrics may be compared to the one or more predicted changes tothe one or more portions of the agent characteristics information. Insome of the various embodiments, one or more models, model portions, orsub-models may be selected by employing a similar process as describedwith regard to one or more of blocks 906, 908, or others, and the one ormore selected models, model portions, or sub-models may be employed,executed, or traversed to evaluate the one or more obtained metricsbased on the one or more predicted changes. In some embodiments, thecomparison or evaluation may include generating a total expectedobjective performance, a calibration amount, an average calibrationamount, or others, as described with regard to one or more processes ofsystem 400. In some embodiments, a calibration amount of zero mayrepresent that the one or more metrics match the one or more predictedchanges to the one or more portions of the agent characteristicsinformation, and non-zero calibration amount may indicate that one ormore measured objective performances in one or more metrics divergedfrom the one or more predicted changes to the one or more agentcharacteristics.

At decision block 1112, in one or more of the various embodiments, ifone or more metrics diverge from the one or more predicted changes tothe one or more portions of the agent characteristics information,control may flow to block 1114; otherwise, control may return to block1104 because the system is appropriately calibrated.

At block 1114, in one or more of the various embodiments, one or morealerts may be provided to one or more performance monitors assigned tothe agent to notify the one or more performance monitors that theagent's performance has diverged from the agent's expected performance.In some of the various embodiments, the one or more performance monitorsmay be assigned to the agent as described with regard to one or moreprocesses of system 400. In some embodiments, the one or moreperformance agents may communicate with the agent or one or moreentities that supervise the agent to obtain further informationassociated with the divergence. In some embodiments, the one or moreperformance agents may modify one or more components of one or moremodels based on the divergence to calibrate the one or more resourceconsumption control computers and improve performance criterion. Block1114 may be optional because one or more performance monitors may notyet or may not be assigned to the agent.

At block 1116, in one or more of the various embodiments, the predictedinterval energy expenditure amount may be modified based on theevaluation of the comparison of the one or more obtained metrics to theone or more predicted changes to the one or more portions of the agentcharacteristics information. In some of the various embodiments, thepredicted interval energy expenditure amount may be modified based onthe calibration amount, average calibration amount, or others asdescribed with regard to one or more processes of system 400.

At block 1118, in one or more of the various embodiments, the modifiedpredicted interval energy expenditure amount may be transformed intomodified resource consumption information. In some of the variousembodiments, the modified resource consumption information may begenerated by executing one or more portions of one or more of blocks1008, 1010, 1012, 1014, or others based on the modified predictedinterval energy expenditure amount instead of the predicted intervalenergy expenditure amount.

At block 1120, in one or more of the various embodiments, one or moremodified resource consumption instructions may be provided to the agentbased on one or more portions of the modified resource consumptioninformation. In some of the various embodiments, the one or moremodified resource consumption instructions may be provided to the agentas described with regard to block 1016 based on one or more portions ofthe modified resource consumption information instead of the resourceconsumption information.

In some embodiments, process 1100 may continue operating until thecontrol session terminates or a user configures process 1100 toterminate operation. Next, control may be returned to a calling process.

FIG. 12 shows a logical flowchart of example process 1200 for evaluatingobtained metrics associated with a control session. One or more portionsof process 1200 may be performed by one or more engines in one or moreclient computers or network computers (for example: one or moreperformance tracking engines 218, performance monitor engines 222, orothers in one or more client computers 200; one or more resourceclassification engines 318, resource recipe generation engines 322,consumption control engines 324, metrics analysis engines 326, or othersin one or more network computers 300; or others), such as one or moreclient or network computers associated with or included in one or moreagents 402, performance monitors 404, resource consumption controlcomputer 406, or others. In one or more of the various embodiments, oneor more portions of process 1200 may correspond to or be included in oneor more of blocks 804, 806, 808, 810, 902, 904, 910, 912, 914, 1002,1008, 1010, 1012, 1014, 1016, 1104, 1108, 1110, 1112, 1114, or others.In some of the various embodiments, after a start block, at block 1202,one or more metrics associated with the control session may be obtained.In some embodiments, the one or more metrics may be based on one or moreactions of the agent (for example, one or more actions performedresponsive to the one or more resource consumption instructions,resource consumption information, objective performance information,mouse clicks, types of clicks, mouse hover time, opens, visits,refreshes, timing of actions, most-recent login time, quantities ofactions, or others). In some embodiments, the one or more metrics may beobtained as the one or more metrics are accumulated or generated, at apredetermined phase in each interval, at a predetermined phase in eachperiod, when the agent connects to a network, or others. In someembodiments, the one or more metrics may indicate the amounts of one ormore resource types consumed by the agent in one or more intakesessions, intervals, or others, may indicate feedback associated withone or more activities (for example, one or more weights, performanceratings, material makeups, activity or training intensity, activity ortraining duration, or others), or others. In some embodiments, one ormore portions of the one or more metrics may be provided via a userinterface (for example, a web page, application, or others) in variousforms, such as email, user interface (UI) notification, instant message,or others. In other embodiments, one or more portions of the one or moremetrics may be provided or generated by one or more sensors or sensorinterfaces (for example, one or more cameras 240, video interfaces 242,sensor interfaces 262, or others) associated with the agent (forexample, one or more sensors or sensor interfaces that are included inclient computer 200, that are in communication with client computer 200,or others).

In one or more of the various embodiments, the one or more metrics mayinclude resource consumption information that may include reportedamounts of specific units, normalized units, or others for each consumedresource type. In some of the various embodiments, the reported amountsof specific or normalized units for each consumed resource type mayindicate the reported amounts of specific or normalized units for eachconsumed resource type over the entirety of the complete intervals inthe qualifying periods, over the entirety of the complete intervals ineach qualifying period, over each complete interval, over each intakesession in each complete interval, or others. Accordingly, in someembodiments, one or more resource consumption control computers mayfacilitate the agent providing one or more portions of one or moremetrics in specific units and another one or more portions of the one ormore metrics in normalized units to alleviate the agent of a requirementto transform specific units into normalized units or normalized unitsinto specific units, thereby improving responsiveness of the system tothe agent.

At decision block 1204, in one or more of the various embodiments, ifone or more portions of the one or more metrics include one or moreamounts in specific units, control may flow to block 1206; otherwise,control may flow to block 1208.

At block 1206, in one or more of the various embodiments, the reportedamounts of specific units for each consumed resource type may betransformed into amounts of normalized units reportedly consumed onaverage per interval for the consumed resource type based on aspecific-units needs amounts for the consumed resource type and anormalized-units needs amount for the resource type (for example, needsamounts generated as described with regard to blocks 1010, 1012, orothers). In some of the various embodiments, reported amounts ofspecific or normalized units consumed on average per interval may begenerated based on the reported amounts of specific or normalized unitsconsumed for each consumed resource type and the number of completeintervals.

For example, the reported amounts of specific or normalized unitsconsumed for each consumed resource type over the entirety of thecomplete intervals in the qualifying periods may be divided by thenumber of complete intervals in the qualifying periods to generate thereported amounts of specific or normalized units consumed on average perinterval.

In one or more of the various embodiments, the reported amounts ofspecific or normalized units consumed on average per interval for eachconsumed resource type may be transformed into total reported amounts ofnormalized units consumed on average per interval based on thespecific-units needs amounts for each resource type (for example: theaverage-interval specific-units needs amount for the resource typegenerated at block 1010 for the qualifying period or the completeintervals; the particular-interval specific-units needs amounts for theresource type generated at block 1010 for the qualifying period or thecomplete intervals; or others) and the normalized-units needs amountsfor each consumed resource type (for example: the average-intervalnormalized-units needs amount for the resource type generated at block1012 for the qualifying period or the complete intervals; theparticular-interval normalized-units needs amount for the resource typegenerated at block 1012 for the qualifying period or the completeintervals; or others). In some of the various embodiments, the reportedamounts of specific units consumed on average per interval for aconsumed resource type may be divided by a ratio of the specific-unitsneeds amount for the consumed resource type to the normalized-unitsneeds amount for the consumed resource type (for example, thespecific-units needs amount for the consumed resource type divided bythe normalized-units needs amount for the consumed resource type orothers), and the result may be added to the reported amounts ofnormalized units consumed on average per interval for the consumedresource type to generate the total reported amounts of normalized unitsconsumed on average per interval for the consumed resource type. In someembodiments, because the specific-units needs amount or thenormalized-units needs amount for each of one or more resource types maybe different for different intervals, an average of the specific-unitsneeds amounts for the resource type for the complete intervals in thequalifying period and the average of the normalized-units needs amountsfor the resource type for the complete intervals in the qualifyingperiod may be employed to generate the ratio. In some embodiments, thetotal amounts of normalized units reportedly consumed on average perinterval may be generated for each consumed resource type.

At block 1208, in one or more of the various embodiments, the totalamounts of normalized units reportedly consumed on average per intervalfor each consumed resource type may be transformed into total amounts ofspecific units reportedly consumed on average per interval for eachconsumed resource type based on the amounts of normalized unitsreportedly consumed on average per interval for one or more otherconsumed resource types. In some of the various embodiments, the totalamounts of normalized units reportedly consumed on average per intervalfor a consumed resource type may be multiplied by a conversion factorassociated with the consumed resource type (for example, the totalnumber of reportedly consumed carbohydrate servings multiplied by theaverage number of grams expected to be included in a serving ofcarbohydrates or others), and the result may be summed with the numberof specific units for the consumed resource type that are expected to beincluded in the total amounts of normalized units reportedly consumed onaverage per interval for each other consumed resource type (for example,the number expected based on one or more resource models 420 in resourcemodel repository 418, such as resource model 700), thereby facilitatinggenerating total amounts of specific units reportedly consumed onaverage per interval for the consumed resource type. In otherembodiments, the amounts of normalized units reportedly consumed onaverage per interval for a consumed resource type may be multiplied by aconversion factor associated with the consumed resource type and summedwith the amounts of specific units reportedly consumed on average perinterval for the consumed resource type, and the result may be summedwith the number of specific units for the consumed resource type thatare expected to be included in the total amounts of normalized unitsreportedly consumed on average per interval for each other consumedresource type, thereby facilitating generating the total amounts ofspecific units reportedly consumed on average per interval for theconsumed resource type. In some embodiments, the total amounts ofspecific units reportedly consumed on average per interval may begenerated for each consumed resource type.

At block 1210, in one or more of the various embodiments, a predictedchange in one or more agent characteristics over two or more qualifyingperiods may be generated based on a predicted energy expenditure amounton average per interval and the total amounts of specific unitsreportedly consumed on average per interval for the multiple resourcetypes. In some of the various embodiments, the total amounts of specificunits reportedly consumed on average per interval for each of theconsumed resource types may be multiplied by the amount of energyexpected to be included in each specific unit of the consumed resourcetype to generate an amount of energy reportedly consumed on average perinterval for each of the consumed resource types, and the reportedamounts of energy of the consumed resource types may be summed toprovide a total amount of energy reportedly consumed on average perinterval.

In one or more of the various embodiments, an average expected objectiveperformance per interval (for example, an average expected weight lossper day, an average expected weight gain per day, or others) based onthe predicted interval energy expenditure amount, the amount of energyreportedly consumed on average per interval or the consumption result,the number of complete intervals, the phase in each period at which eachcomplete interval occurs, or others. In some of the various embodiments,the predicted interval energy expenditure amount may be subtracted fromthe amount of energy reportedly consumed on average per interval togenerate a reported energy divergence amount on average per interval. Insome embodiments, the average energy divergence amount per interval maybe transformed into an average expected objective performance perinterval (as measured in the units employed for the objectiveperformance information, such as pounds, kilograms, a scaled rating suchas one to five, or others). For example, the average energy divergenceamount per interval may be measured in calories, and the average energydivergence amount per interval may be divided by 7,700 to transform theaverage energy divergence amount per interval into the average expectedobjective performance per interval (for example, weight loss perinterval or others) as measured in kilograms.

In one or more of the various embodiments, an average complete intervalmay be generated for each qualifying period based on the phase in eachqualifying period at which each complete interval occurs. For example,for a qualifying period of a week that employs intervals of days withthe qualifying period starting on a Wednesday, Wednesday may be intervalone, Thursday may be interval two, Friday may be interval three,Saturday may be interval four, Sunday may be interval five, Monday maybe interval six, Tuesday may be interval seven, and, with completeintervals of Wednesday, Friday, Saturday, and Tuesday, the averagecomplete interval may be 3.75 ((1+3+4+7)/4=3.75). In some embodiments,the number of intervals between the average complete interval of themost recent qualifying period and the average complete interval of thefirst qualifying period may be counted, including one of the first ormost recent average complete interval. For example, when the most recentqualifying period is the week immediately following the first qualifyingperiod and both the most recent and the first qualifying periods haveaverage complete intervals of 3.75, the number of intervals between theaverage complete intervals may be 7. In some embodiments, the averageexpected objective performance per interval may be multiplied by thenumber of intervals between the average complete intervals to generatethe predicted change in the one or more agent characteristics over thequalifying periods.

At block 1212, in one or more of the various embodiments, one or moreaverage values for one or more agent characteristics in the firstqualifying period may be compared to one or more average values for oneor more agent characteristics in the most recent qualifying period togenerate one or more obtained changes in one or more agentcharacteristics. In some of the various embodiments, the one or moremetrics may include objective performance information (for example, theweight of the agent as measured each day in a week or others) for eachcomplete interval in each qualifying period. In some embodiments, theobjective performance information for the intervals in a qualifyingperiod may be averaged to generate an average objective performance forthe qualifying period. For example, the sum of the weight of the agentat each complete interval in a qualifying period may be divided by thenumber of complete intervals in the qualifying period to generate theaverage objective performance for the qualifying period. In someembodiments, the average objective performance for the first qualifyingperiod being evaluated (for example, the first-in-time qualifying periodbeing evaluated or others) may be generated for each qualifying period,the first-in-time and most recent of the qualifying periods beingevaluated, or others. In some embodiments, the average objectiveperformance for the first qualifying period may be subtracted from theaverage objective performance of the most recent qualifying period togenerate an objective performance change over the qualifying periodsbeing evaluated.

At block 1214, in one or more of the various embodiments, the predictedchange in the one or more agent characteristics over the qualifyingperiods may be compared to the one or more obtained changes in the oneor more agent characteristics. In some of the various embodiments, thepredicted change in the one or more agent characteristics over thequalifying periods being evaluated may be subtracted from the objectiveperformance change over the qualifying periods being evaluated togenerate an objective performance divergence. In some embodiments, theobjective performance divergence may be divided by the number ofintervals between the average complete intervals for the most recentperiod and the average complete intervals in the first period togenerate an objective performance divergence on average per interval. Insome embodiments, the objective performance divergence on average perinterval may be transformed into a calibration amount (as measured inthe units employed for the predicted interval energy expenditure amount,such as calories or others). For example, the objective performancedivergence on average per interval may be measured in kilograms, and theobjective performance divergence on average per interval may be dividedby 7,700 to generate the calibration amount as measured in calories. Inother embodiments, the objective performance divergence on average perinterval may be summed with one or more previously generated objectiveperformance divergences on average per interval (for example, up to apredetermined number of previous objective performance divergences thatmay have been generated employing similar processes as described withregard to the objective performance divergence on average per intervalbased on qualifying periods that may be prior to the most recentqualifying period, such as three of the immediately preceding objectiveperformance divergences or others), and the sum may be divided by thenumber of objective performance divergences being evaluated (includingboth the one or more previous objective performance divergences and theobjective performance divergence) to generate an average objectiveperformance divergence for a trailing window defined by the number ofprevious objective performance divergences employed, with thecalibration amount being generated by dividing the average objectivedivergence by 7,700 or other transformation amounts. In someembodiments, a calibration amount of zero may represent that theobjective performance information (or trailing window of the objectiveperformance information) matches the one or more predicted changes tothe one or more portions of the agent characteristics information, andnon-zero calibration amount may indicate that the objective performanceinformation (or trailing window of the objective performanceinformation) diverged from the one or more predicted changes to the oneor more agent characteristics.

At decision block 1216, in one or more of the various embodiments, ifthe objective performance information (or trailing window of theobjective performance information) diverged from the one or morepredicted changes to the one or more agent characteristics, control mayflow to block 1218; otherwise, control may flow to block 1202 tocontinue monitoring the agent in the control session.

At block 1218, in one or more of the various embodiments, one or morenotifications may be provided based on the divergence detected in thecomparative evaluation of the objective performance information (ortrailing window of the objective performance information) and the one ormore predicted changes to the one or more agent characteristics. In someof the various embodiments, the one or more notifications may beprovided to one or more performance monitors assigned to the agent,entities that supervise the agent, engines tasked with modifying thecontrol session, the agent, or others.

In some embodiments, process 1200 may continue operating until thecontrol session terminates or a user configures process 1200 toterminate operation. Next, control may be returned to a calling process.

FIG. 13 illustrates a logical flow diagram of example process 1300 forassigning a performance monitor to an agent. One or more portions ofprocess 1300 may be performed by one or more engines in one or moreclient computers or network computers (for example: one or moreperformance tracking engines 218, performance monitor engines 222, orothers in one or more client computers 200; one or more resourceclassification engines 318, resource recipe generation engines 322,consumption control engines 324, metrics analysis engines 326, or othersin one or more network computers 300; or others), such as one or moreclient or network computers associated with or included in one or moreagents 402, performance monitors 404, resource consumption controlcomputer 406, or others. In one or more of the various embodiments, oneor more portions of process 1100 may correspond to or be included in oneor more of blocks 802, 902, 904, 1002, or others. In some of the variousembodiments, after a start block, at block 1302, agent characteristicsinformation associated with an agent may be obtained to initialize orlaunch a control session, such as a control session to control resourceconsumption by an agent. In some embodiments, the agent, an entity thatsupervises the agent, a performance monitor temporarily assigned to theagent, or others may communicate one or more portions of the agentcharacteristics information over one or more networks to one or moreresource consumption control computers 406. In some embodiments, one ormore portions of the agent characteristics information may be providedvia a user interface (for example, a web page, application, or others)in various forms, such as email, user interface (UI) notification,instant message, or others. In other embodiments, one or more portionsof the agent characteristics information may be provided or generated byone or more sensors or sensor interfaces (for example, one or morecameras 240, video interfaces 242, sensor interfaces 262, or others)associated with the agent (for example, one or more sensors or sensorinterfaces that are included in client computer 200, that are incommunication with client computer 200, or others).

At block 1304, in one or more of the various embodiments, one or moreagent data objects associated with the agent may be generated based onone or more portions of the agent characterization information. In someof the various embodiments, the one or more agent data objects may begenerated or populated as described with regard to one or more processesof system 400, one or more processes described with regard to one ormore data structures described with regard to FIG. 6 or 7, block 904, orothers.

At block 1306, in one or more of the various embodiments, a group statusof the agent may be evaluated based on one or more portions of the agentcharacterization information. In some of the various embodiments, one ormore portions of the agent characteristics information may be providedin a comma-separated values (CSV) file that includes one or moreportions of agent characteristics information associated with multipleagents in one or more groups. For example, an entity that supervises theagent may provide a CSV file that includes a roster of the supervisedagents in one or more groups or sub-groups. Accordingly, in someembodiments, the file may indicate group status information associatedwith the agent. In other embodiments, the agent's email address may beevaluated and compared to a list or parsed to generate the group statusinformation.

At decision block 1308, in one or more of the various embodiments, ifthe group status of the agent indicates that the agent is part of orassociated with one or more groups or sub-groups, control may flow toblock 1310; otherwise, control may flow to block 1312.

At block 1310, in one or more of the various embodiments, the one ormore agent data objects may be associated with one or more other agentdata objects that are associated with one or more other agents in theone or more groups or sub-groups to which the agent belongs or withwhich the agent is associated based on the agent characteristicsinformation. In some of the various embodiments, the one or more agentdata objects may include one or more group or sub-group fields that maybe populated with one or more identifiers (for example, self-referentialidentifiers, referential identifiers, or others) that indicate the groupstatus, the one or more groups or sub-groups, or the other agents.

At block 1312, in one or more of the various embodiments, one or moreperformance monitors may be assigned to the agent based on the one ormore agent data objects and one or more portions of the agentcharacteristics information. In some of the various embodiments, if theagent is associated with one or more groups or sub-groups, one or moreperformance monitors may be assigned to each agent in the one or moregroups or sub-groups, thereby facilitating horizontal access toinformation. In some embodiments, if the agent is not associated withone or more groups or sub-groups, one or more performance monitors maybe assigned to the agent arbitrarily, based on geographical or logicalregion or territory, based on activity type, agent type, objectives, orothers.

In some embodiments, process 1300 may continue operating until thecontrol session terminates, the control session has fully launched, or auser configures process 1300 to terminate operation. Next, control maybe returned to a calling process.

FIG. 14 shows a logical flowchart of example process 1400 for generatinga resource data object for a new resource. One or more portions ofprocess 1400 may be performed by one or more engines in one or moreclient computers or network computers (for example: one or moreperformance tracking engines 218, performance monitor engines 222, orothers in one or more client computers 200; one or more resourceclassification engines 318, resource recipe generation engines 322,consumption control engines 324, metrics analysis engines 326, or othersin one or more network computers 300; or others), such as one or moreclient or network computers associated with or included in one or moreagents 402, performance monitors 404, resource consumption controlcomputer 406, or others. In one or more of the various embodiments,after a start block, at block 1402, resource characteristics informationmay be obtained for a new resource. In some of the various embodiments,an agent, an entity that supervises the agent, a performance monitor,administrator, or others may communicate one or more portions of theresource characteristics information over one or more networks to one ormore resource consumption control computers 406. In some embodiments,one or more portions of the resource characteristics information may beprovided via a user interface (for example, a web page, application, orothers) in various forms, such as email, user interface (UI)notification, instant message, or others. In other embodiments, one ormore portions of the resource characteristics information may beprovided or generated by one or more sensors or sensor interfaces (forexample, one or more cameras 240, video interfaces 242, sensorinterfaces 262, or others). In some embodiments, the resourcecharacteristics information may include one or more energy sources (forexample, protein, carbohydrates, fats, or others) in terms ofpercentages (for example, a given amount of a resource may derivevarious percentages of its energy from different energy sources) orspecific units (for example, grams or others).

At block 1404, in one or more of the various embodiments, a resourcetype to evaluate based on the resource characteristics information maybe selected. In some of the various embodiments, the resource typeselected may be arbitrary, based on a predetermined order, based on abest preliminary guess, or others.

At block 1406, in one or more of the various embodiments, one or moreconditions of the selected resource type may be evaluated based on oneor more portions of the resource characteristics information. In some ofthe various embodiments, each resource type may have one or moreconditions that, if satisfied, indicate that the one or more portions ofthe resource characteristics information may match with the resourcetype. In some embodiments, a condition may include that the one or moreportions of the resource characteristics information has not matchedwith a higher-ranking resource type in a hierarchy of resource types.For example, vegetables may have highest priority, carbohydrates mayhave the second highest priority, proteins may have the third highestpriority, and fats may have the lowest priority. Accordingly, in someembodiments, a resource that satisfies conditions of multiple resourcetypes may only match with the satisfied resource type that has thehighest priority. In some embodiments, a condition may include most ofthe percentage of the one or more energy sources of the resource beingthe resource type (for example, the resource derives a greaterpercentage of its energy from the evaluated resource type than thepercentage of its energy derived from each other resource type). In someembodiments, a condition may include the highest specific units amountof the one or more energy sources of the resource being the resourcetype. In some embodiments, one or more conditions must be satisfied, oneor more of multiple conditions must be satisfied, or others.

At decision block 1408, in one or more of the various embodiments, ifthe one or more portions of the resource characteristics informationmatch with the selected resource type, control may flow to block 1412;otherwise, control may flow to block 1410.

At block 1410, in one or more of the various embodiments, anotherresource type is selected to evaluate. In some of the variousembodiments, the resource type selected may be arbitrary, based on apredetermined order, based on a best preliminary guess, or others.

At block 1412, in one or more of the various embodiments, specific-unitsamounts for the resource may be transformed into normalized-unitsamounts for the new resource based on one or more portions of theresource characteristics information and the matched resource type. Insome of the various embodiments, an expected number of normalized unitsper a predetermined number of specific units may be based on historicalanalysis provided by a third party, average sample analysis, user input,or others. In some embodiments, the energy source information may beemployed to generate the number of normalized units per number ofspecific units. In other embodiments, the number of normalized units pernumber of specific units may be standard based on the average orexpected number for the matched resource type (for example, 25 grams ofa resource that is a resource type of protein may equate to one servingof protein or others).

At block 1414, in one or more of the various embodiments, one or moreresource data objects may be generated for the resource based on one ormore portions of the resource characteristics information and theevaluation. In some of the various embodiments, the one or more resourcedata objects may be generated or populated employing one or more similarprocesses as described with regard to one or more processes of system400, one or more processes described with regard to one or more datastructures described with regard to FIG. 6 or 7, or others. For example,resource model 700 may be appended with the data structure, and the datastructure may be populated with the number of normalized units, thenumber of specified units per the number of normalized units, theresource name, one or more characteristics (for example, the resourcetype, the energy sources in terms of percentages or specific units, orothers).

In some embodiments, process 1400 may continue operating until thecontrol session terminates or a user configures process 1400 toterminate operation. Next, control may be returned to a calling process.

FIG. 15 illustrates a logical flow diagram of example process 1500 forgenerating a resource recipe. One or more portions of process 1500 maybe performed by one or more engines in one or more client computers ornetwork computers (for example: one or more performance tracking engines218, performance monitor engines 222, or others in one or more clientcomputers 200; one or more resource classification engines 318, resourcerecipe generation engines 322, consumption control engines 324, metricsanalysis engines 326, or others in one or more network computers 300; orothers), such as one or more client or network computers associated withor included in one or more agents 402, performance monitors 404,resource consumption control computer 406, or others. In one or more ofthe various embodiments, after a start block, at block 1502, one or moreresource data objects having resource characteristics informationassociated with a resource may be obtained (for example, one or moreresource data structures in resource model 700 or others).

At block 1504, in one or more of the various embodiments, another one ormore resource data objects having resource characteristics informationassociated with another resource may be obtained (for example, anotherone or more data structures in resource model 700 or others).

At block 1506, in one or more of the various embodiments, one or moreportions of the resource characteristics information of the one or moreother resource data objects may be evaluated based on one or moreportions of the resource characteristics information of the one or moreresource data objects. In some of the various embodiments, the resourceand the other resource may be compatible with each other if one or moreportions of their respective resource characteristic informationoverlaps, matches, satisfies one or more conditions, fails to overlap,fails to match, or others. For example, the resource and the otherresource may be compatible with each other if their respective resourcecharacteristic information indicates that they may both be breakfastfoods, if only one of them takes a large amount of time or effort toprepare, if they are of different resource types, if their combinationcorrelates to a predicted high likelihood of agent or control sessionsuccess, or others. In some embodiments, compatibility may vary based ongeographic region for which the recipe is intended.

At decision block 1508, in one or more of the various embodiments, ifthe resource and the other resource are compatible with each other,control may flow to decision block 1510; otherwise, control may flow toblock 1504 to substitute one or more of the resource or the otherresource with a further resource to evaluate.

At decision block 1510, in one or more of the various embodiments, ifthe recipe has enough resources, control may flow to block 1512;otherwise, control may flow to block 1504 to select a further resourceto evaluate in combination with the resource and the other resource. Insome of the various embodiments, one or more configuration files, rules,custom scripts, or others may define one or more thresholds for aminimum number of resources to include in a recipe based on one or morefactors, such as whether the recipe is a snack or a meal recipe, thetype of snack or meal (for example, standard, pre-workout, post-workout,quick preparation, normal preparation, gourmet preparation, or others).

At block 1512, in one or more of the various embodiments, one or moreportions of the resource characteristics information of each resource inthe recipe may be evaluated to select one or more resource consumptionphases to associate with the recipe. In some of the various embodiments,each phase associated with each of the resources in the recipe may beselected. In some embodiments, one or more phases associated with therecipe may vary based on geographic region for which the recipe isintended.

At block 1514, in one or more of the various embodiments, one or moreresource recipes may be generated based on one or more portions of theresource characteristics information associated with the selectedresources and based on the one or more selected resource consumptionphases. In some of the various embodiments, generating the recipe mayinclude generating or populating one or more data objects associatedwith the recipe. In some embodiments, the one or more recipe dataobjects may be generated or populated employing one or more similarprocesses as described with regard to one or more processes of system400, one or more processes described with regard to one or more datastructures described with regard to FIG. 6 or 7, or others. In someembodiments, one or more attributes of the one or more recipe dataobjects may indicate one or more restrictions (for example, allergies,dietary choices or restrictions, intake type, geographical associations,intake phases, or others).

In some embodiments, process 1500 may continue operating until a controlsession terminates, all combinations of resources have been evaluated,until a threshold number of recipes have been generated, or a userconfigures process 1500 to terminate operation. Next, control may bereturned to a calling process.

It will be understood that each block of the flowchart illustration, andcombinations of blocks in the flowchart illustration, can be implementedby computer program instructions. These program instructions may beprovided to one or more processors to produce a machine, such that theinstructions, which execute on the one or more processors, create meansfor implementing the actions specified in the flowchart block or blocks.The computer program instructions may be executed by the one or moreprocessors to cause a series of operational steps to be performed by theone or more processors to produce a computer-implemented process suchthat the instructions, which execute on the one or more processors toprovide steps for implementing the actions specified in the flowchartblock or blocks. The computer program instructions may also cause atleast some of the operational steps shown in the blocks of the flowchartto be performed in parallel or concurrently by the one or moreprocessors or one or more computers. Moreover, some of the steps mayalso be performed across more than one processor or computer. Inaddition, one or more blocks or combinations of blocks in the flowchartillustration may also be performed concurrently with other blocks orcombinations of blocks, or even in a different sequence than illustratedwithout departing from the scope or spirit of the invention.

Accordingly, blocks of the flowchart illustration support combinationsof means for performing the specified actions, combinations of steps forperforming the specified actions and program instruction means forperforming the specified actions. It will also be understood that eachblock of the flowchart illustration, and combinations of blocks in theflowchart illustration, can be implemented by special purpose hardwarebased systems, which perform the specified actions or steps, orcombinations of special purpose hardware and computer instructions. Theforegoing example should not be construed as limiting or exhaustive, butrather, an illustrative use case to show an embodiment of one or more ofthe various embodiments of the invention. Moreover, one or more portionsof one or more embodiments may be modified without departing from theinvention. For example, an agent age below a threshold may indicate thatthe agent is in a development phase that should be evaluated in additionto the performance objectives, such as employing one or more models,model portions, sub-models, or others associated with predicteddevelopment needs in addition or alternative to one or more predictedinterval energy expenditure amounts (for example, employing one or moresimilar processes as described with regard to the predicted energyconsumption needs yet with the one or more additional or alternativemodels, model portions, sub-models, or others).

Further, in one or more embodiments (not shown in the figures), thelogic in the illustrative flowcharts may be executed using one or moreembedded logic hardware devices instead of one or more CPUs, such as anApplication Specific Integrated Circuits (ASICs), Field ProgrammableGate Arrays (FPGAs), Programmable Array Logic chips (PALs), or others.The embedded one or more logic hardware devices may directly executetheir embedded logic to perform actions. In at least one embodiment, oneor more microcontrollers may be arranged as system-on-a-chip (SOCs) todirectly execute their own locally embedded logic to perform actions andaccess their own internal memory and their own external Input and OutputInterfaces (e.g., hardware pins or wireless transceivers) to performactions described herein.

What is claimed as new and desired to be protected by Letters Patent ofthe United States is:
 1. A method for improving performance criterion ina control session, wherein one or more processors in a network computerexecute instructions to perform actions, comprising: employing aconsumption engine to perform further actions, including: obtainingcharacteristics information associated with an agent; generating one ormore outputs based on the characteristics information and a performancemodel, wherein the one or more outputs include a predicted expenditureamount; transforming the predicted expenditure amount intospecific-units amounts of multiple resource types based on thecharacteristics information; transforming the specific-units amount ofeach of the multiple resource types into a normalized-units amount ofeach of the multiple resource types based on one or morenormalized-units amounts of one or more other resource types; providinga consumption instruction to the agent based on the normalized-unitsamounts of the multiple resource types; employing an analysis engine toperform further actions, including: obtaining metrics that are based onthe consumption instruction and a monitoring of the agent; and comparingone or more portions of the metrics to the one or more outputs of theperformance model; modifying the one or more outputs based on thecomparison to increase a correlation between the one or more outputs andthe metrics; and providing a modified consumption instruction to theagent based on the one or more modified outputs.
 2. The method of claim1, wherein the metrics indicate reported amounts of multiple resourcetypes consumed by the agent in multiple intervals and includeperformance feedback that indicates measured values of an agentcharacteristic in the multiple intervals.
 3. The method of claim 1,wherein the metrics include performance feedback that indicates a valueof a measured agent characteristic in each of two or more intervals ineach of two or more periods, and comparing the one or more portions ofthe metrics to the one or more outputs of the performance modelcomprises: evaluating the value of the measured agent characteristic ineach of the two or more intervals in each of the two or more periods toprovide a first agent characteristic amount for a first one of the twoor more periods and a second agent characteristic amount for a secondone of the two or more periods; evaluating the first agentcharacteristic amount based on the second characteristic amount toprovide an obtained amount of change in the agent characteristic; andcomparing the obtained amount of change in the agent characteristic tothe one or more outputs of the performance model, wherein the one ormore outputs of the performance model include a value of a predictedchange in the agent characteristic based on the predicted expenditureamount.
 4. The method of claim 1, wherein the metrics indicatereportedly consumed amounts of specific units of the multiple resourcetypes in multiple intervals and include performance feedback thatindicates a value of a measured change in an agent characteristic, andcomparing the one or more portions of the metrics to the one or moreoutputs of the performance model comprises: transforming the reportedlyconsumed amount of specific units of each of the multiple resource typesinto reportedly consumed amounts of normalized units of each of themultiple resource types based on the specific-units amount of each ofthe multiple resource types and the normalized-units amount of each ofthe multiple resource types; transforming the reportedly consumed amountof normalized units of each of the multiple resource types into a totalreportedly consumed amount of specific units of each of the multipleresource types based on one or more of the reportedly consumed amountsof normalized units of one or more other ones of the multiple resourcetypes; and comparing the value of the measured change in the agentcharacteristic to the one or more outputs of the performance model,wherein the one or more outputs of the performance model include a valueof a predicted change in the agent characteristic based on the predictedexpenditure amount and the total reportedly consumed amounts of specificunits of the multiple resource types.
 5. The method of claim 1, whereinone or more portions of the characteristics information associated withthe agent indicate an impairment status of the agent, and transformingthe predicted expenditure amount into the specific-units amounts of themultiple resource types comprises increasing the specific-units amountof one of the multiple resource types based on the impairment status ofthe agent.
 6. The method of claim 1, wherein providing the consumptioninstruction to the agent comprises: transforming the normalized-unitsamounts of the multiple resource types into resource distributioninformation based on one or more portions of the characteristicsinformation associated with the agent; and transforming one or moreportions of the resource distribution information into the resourceconsumption instruction.
 7. The method of claim 1, further comprisingproviding one or more alerts to one or more performance monitors basedon the comparison of the one or more portions of the metrics to the oneor more outputs of the performance model indicating that performance ofthe agent diverged from performance of one or more groups of agentsassociated with the agent.
 8. The method of claim 1, wherein the metricsare obtained from a client computer of the agent.
 9. A system forimproving performance criterion in a control session, comprising: anetwork computer, comprising: one or more transceivers that communicateover a network; memory that stores at least instructions; and one ormore processors that execute instructions associated with the networkcomputer that cause the one or more processors of the network computerto perform actions, comprising: employing a consumption engine toperform further actions, including: obtaining characteristicsinformation associated with an agent; generating one or more outputsbased on the characteristics information and a performance model,wherein the one or more outputs include a predicted expenditure amount;transforming the predicted expenditure amount into specific-unitsamounts of multiple resource types based on the characteristicsinformation; transforming the specific-units amount of each of themultiple resource types into a normalized-units amount of each of themultiple resource types based on one or more normalized-units amounts ofone or more other resource types; providing a consumption instruction tothe agent based on the normalized-units amounts of the multiple resourcetypes; employing an analysis engine to perform further actions,including:  obtaining metrics that are based on the consumptioninstruction and a monitoring of the agent; and  comparing one or moreportions of the metrics to the one or more outputs of the performancemodel; modifying the one or more outputs based on the comparison toincrease a correlation between the one or more outputs and the metrics;and providing a modified consumption instruction to the agent based onthe one or more modified outputs; and a client computer, comprising: oneor more transceivers that communicate over the network; memory thatstores at least instructions; and one or more processors that executeinstructions associated with the client computer that cause the one ormore processors of the client computer to perform actions, comprising:monitoring one or more actions of the agent; and providing one or moreportions of the metrics based on the monitored actions of the agent. 10.The system of claim 9, wherein the metrics indicate reported amounts ofmultiple resource types consumed by the agent in multiple intervals andinclude performance feedback that indicates measured values of an agentcharacteristic in the multiple intervals.
 11. The system of claim 9,wherein the metrics include performance feedback that indicates a valueof a measured agent characteristic in each of two or more intervals ineach of two or more periods, and comparing the one or more portions ofthe metrics to the one or more outputs of the performance modelcomprises: evaluating the value of the measured agent characteristic ineach of the two or more intervals in each of the two or more periods toprovide a first agent characteristic amount for a first one of the twoor more periods and a second agent characteristic amount for a secondone of the two or more periods; evaluating the first agentcharacteristic amount based on the second characteristic amount toprovide an obtained amount of change in the agent characteristic; and12. The system of claim 9, wherein the metrics indicate reportedlyconsumed amounts of specific units of the multiple resource types inmultiple intervals and include performance feedback that indicates avalue of a measured change in an agent characteristic, and comparing theone or more portions of the metrics to the one or more outputs of theperformance model comprises: transforming the reportedly consumed amountof specific units of each of the multiple resource types into reportedlyconsumed amounts of normalized units of each of the multiple resourcetypes based on the specific-units amount of each of the multipleresource types and the normalized-units amount of each of the multipleresource types; transforming the reportedly consumed amount ofnormalized units of each of the multiple resource types into a totalreportedly consumed amount of specific units of each of the multipleresource types based on one or more of the reportedly consumed amountsof normalized units of one or more other ones of the multiple resourcetypes; and comparing the value of the measured change in the agentcharacteristic to the one or more outputs of the performance model,wherein the one or more outputs of the performance model include a valueof a predicted change in the agent characteristic based on the predictedexpenditure amount and the total reportedly consumed amounts of specificunits of the multiple resource types.
 13. The system of claim 9, whereinone or more portions of the characteristics information associated withthe agent indicate an impairment status of the agent, and transformingthe predicted expenditure amount into the specific-units amounts of themultiple resource types comprises increasing the specific-units amountof one of the multiple resource types based on the impairment status ofthe agent.
 14. The system of claim 9, wherein providing the consumptioninstruction to the agent comprises: transforming the normalized-unitsamounts of the multiple resource types into resource distributioninformation based on one or more portions of the characteristicsinformation associated with the agent; and transforming one or moreportions of the resource distribution information into the resourceconsumption instruction.
 15. The system of claim 9, wherein theinstructions associated with the network computer cause the one or moreprocessors of the network computer to perform further actions,comprising providing one or more alerts to one or more performancemonitors based on the comparison of the one or more portions of themetrics to the one or more outputs of the performance model indicatingthat performance of the agent diverged from performance of one or moregroups of agents associated with the agent.
 16. A processor readablenon-transitory storage media that includes instructions for improvingperformance criterion in a control session, wherein execution of theinstructions by one or more processors performs actions, comprising:employing a consumption engine to perform further actions, including:obtaining characteristics information associated with an agent;generating one or more outputs based on the characteristics informationand a performance model, wherein the one or more outputs include apredicted expenditure amount; transforming the predicted expenditureamount into specific-units amounts of multiple resource types based onthe characteristics information; transforming the specific-units amountof each of the multiple resource types into a normalized-units amount ofeach of the multiple resource types based on one or morenormalized-units amounts of one or more other resource types; providinga consumption instruction to the agent based on the normalized-unitsamounts of the multiple resource types; employing an analysis engine toperform further actions, including: obtaining metrics that are based onthe consumption instruction and a monitoring of the agent; and comparingone or more portions of the metrics to the one or more outputs of theperformance model; modifying the one or more outputs based on thecomparison to increase a correlation between the one or more outputs andthe metrics; and providing a modified consumption instruction to theagent based on the one or more modified outputs.
 17. The media of claim16, wherein the metrics indicate reported amounts of multiple resourcetypes consumed by the agent in multiple intervals and includeperformance feedback that indicates measured values of an agentcharacteristic in the multiple intervals.
 18. The media of claim 16,wherein the metrics include performance feedback that indicates a valueof a measured agent characteristic in each of two or more intervals ineach of two or more periods, and comparing the one or more portions ofthe metrics to the one or more outputs of the performance modelcomprises: evaluating the value of the measured agent characteristic ineach of the two or more intervals in each of the two or more periods toprovide a first agent characteristic amount for a first one of the twoor more periods and a second agent characteristic amount for a secondone of the two or more periods; evaluating the first agentcharacteristic amount based on the second characteristic amount toprovide an obtained amount of change in the agent characteristic; andcomparing the obtained amount of change in the agent characteristic tothe one or more outputs of the performance model, wherein the one ormore outputs of the performance model include a value of a predictedchange in the agent characteristic based on the predicted expenditureamount.
 19. The media of claim 16, wherein the metrics indicatereportedly consumed amounts of specific units of the multiple resourcetypes in multiple intervals and include performance feedback thatindicates a value of a measured change in an agent characteristic, andcomparing the one or more portions of the metrics to the one or moreoutputs of the performance model comprises: transforming the reportedlyconsumed amount of specific units of each of the multiple resource typesinto reportedly consumed amounts of normalized units of each of themultiple resource types based on the specific-units amount of each ofthe multiple resource types and the normalized-units amount of each ofthe multiple resource types; transforming the reportedly consumed amountof normalized units of each of the multiple resource types into a totalreportedly consumed amount of specific units of each of the multipleresource types based on one or more of the reportedly consumed amountsof normalized units of one or more other ones of the multiple resourcetypes; and comparing the value of the measured change in the agentcharacteristic to the one or more outputs of the performance model,wherein the one or more outputs of the performance model include a valueof a predicted change in the agent characteristic based on the predictedexpenditure amount and the total reportedly consumed amounts of specificunits of the multiple resource types.
 20. The media of claim 16, whereinone or more portions of the characteristics information associated withthe agent indicate an impairment status of the agent, and transformingthe predicted expenditure amount into the specific-units amounts of themultiple resource types comprises increasing the specific-units amountof one of the multiple resource types based on the impairment status ofthe agent.
 21. The media of claim 16, wherein providing the consumptioninstruction to the agent comprises: transforming the normalized-unitsamounts of the multiple resource types into resource distributioninformation based on one or more portions of the characteristicsinformation associated with the agent; and transforming one or moreportions of the resource distribution information into the resourceconsumption instruction.
 22. The media of claim 16, wherein the metricsare obtained from a client computer of the agent.
 23. A network computerfor improving performance criterion in a control session, comprising:one or more transceivers that communicate over a network; memory thatstores at least instructions; and one or more processors that executeinstructions that cause the one or more processors to perform actions,including: employing a consumption engine to perform further actions,including: obtaining characteristics information associated with anagent; generating one or more outputs based on the characteristicsinformation and a performance model, wherein the one or more outputsinclude a predicted expenditure amount; transforming the predictedexpenditure amount into specific-units amounts of multiple resourcetypes based on the characteristics information; transforming thespecific-units amount of each of the multiple resource types into anormalized-units amount of each of the multiple resource types based onone or more normalized-units amounts of one or more other resourcetypes; providing a consumption instruction to the agent based on thenormalized-units amounts of the multiple resource types; employing ananalysis engine to perform further actions, including: obtaining metricsthat are based on the consumption instruction and a monitoring of theagent; and comparing one or more portions of the metrics to the one ormore outputs of the performance model; modifying the one or more outputsbased on the comparison to increase a correlation between the one ormore outputs and the metrics; and providing a modified consumptioninstruction to the agent based on the one or more modified outputs. 24.The network computer of claim 23, wherein the metrics indicate reportedamounts of multiple resource types consumed by the agent in multipleintervals and include performance feedback that indicates measuredvalues of an agent characteristic in the multiple intervals.
 25. Thenetwork computer of claim 23, wherein the metrics include performancefeedback that indicates a value of a measured agent characteristic ineach of two or more intervals in each of two or more periods, andcomparing the one or more portions of the metrics to the one or moreoutputs of the performance model comprises: evaluating the value of themeasured agent characteristic in each of the two or more intervals ineach of the two or more periods to provide a first agent characteristicamount for a first one of the two or more periods and a second agentcharacteristic amount for a second one of the two or more periods;evaluating the first agent characteristic amount based on the secondcharacteristic amount to provide an obtained amount of change in theagent characteristic; and comparing the obtained amount of change in theagent characteristic to the one or more outputs of the performancemodel, wherein the one or more outputs of the performance model includea value of a predicted change in the agent characteristic based on thepredicted expenditure amount.
 26. The network computer of claim 23,wherein the metrics indicate reportedly consumed amounts of specificunits of the multiple resource types in multiple intervals and includeperformance feedback that indicates a value of a measured change in anagent characteristic, and comparing the one or more portions of themetrics to the one or more outputs of the performance model comprises:transforming the reportedly consumed amount of specific units of each ofthe multiple resource types into reportedly consumed amounts ofnormalized units of each of the multiple resource types based on thespecific-units amount of each of the multiple resource types and thenormalized-units amount of each of the multiple resource types;transforming the reportedly consumed amount of normalized units of eachof the multiple resource types into a total reportedly consumed amountof specific units of each of the multiple resource types based on one ormore of the reportedly consumed amounts of normalized units of one ormore other ones of the multiple resource types; and comparing the valueof the measured change in the agent characteristic to the one or moreoutputs of the performance model, wherein the one or more outputs of theperformance model include a value of a predicted change in the agentcharacteristic based on the predicted expenditure amount and the totalreportedly consumed amounts of specific units of the multiple resourcetypes.
 27. The network computer of claim 23, wherein one or moreportions of the characteristics information associated with the agentindicate an impairment status of the agent, and transforming thepredicted expenditure amount into the specific-units amounts of themultiple resource types comprises increasing the specific-units amountof one of the multiple resource types based on the impairment status ofthe agent.
 28. The network computer of claim 23, wherein providing theconsumption instruction to the agent comprises: transforming thenormalized-units amounts of the multiple resource types into resourcedistribution information based on one or more portions of thecharacteristics information associated with the agent; and transformingone or more portions of the resource distribution information into theresource consumption instruction.
 29. The network computer of claim 23,wherein the instructions cause the one or more processors to performfurther actions, comprising providing one or more alerts to one or moreperformance monitors based on the comparison of the one or more portionsof the metrics to the one or more outputs of the performance modelindicating that performance of the agent diverged from performance ofone or more groups of agents associated with the agent.
 30. The networkcomputer of claim 23, wherein the metrics are obtained from a clientcomputer of the agent.